From edc044fedeb8edc93bf1d5a020b579eafd4d77b8 Mon Sep 17 00:00:00 2001 From: KangyuWeng Date: Mon, 20 Jan 2025 16:14:51 -0500 Subject: [PATCH 1/4] Add new papers using deepxde --- docs/user/research.rst | 225 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 222 insertions(+), 3 deletions(-) diff --git a/docs/user/research.rst b/docs/user/research.rst index e578e3b7d..635ad2f77 100644 --- a/docs/user/research.rst +++ b/docs/user/research.rst @@ -3,7 +3,7 @@ Research DeepXDE has been used in -- > 180 universities, e.g., +- > 230 universities, e.g., `Harvard University `_, `Massachusetts Institute of Technology `_, `Stanford University `_, @@ -14,6 +14,7 @@ DeepXDE has been used in `Johns Hopkins University `_, `University of Pennsylvania `_, `Tsinghua University `_, + `University of Toronto `_, `California Institute of Technology `_, `Princeton University `_, `Cornell University `_, @@ -21,12 +22,14 @@ DeepXDE has been used in `Nanyang Technological University `_, `University of California, San Diego `_, `Peking University `_, + `University of Amsterdam `_, `New York University Abu Dhabi `_, `University of British Columbia `_, `University of Copenhagen `_, `KU Leuven `_, `University of Pittsburgh `_, `Zhejiang University `_, + `Shanghai Jiao Tong University `_, `University of Texas at Austin `_, `Leiden University `_, `University of Minnesota `_, @@ -34,6 +37,11 @@ DeepXDE has been used in `University of Chinese Academy of Sciences `_, `Georgia Institute of Technology `_, `Boston University `_, + `University of Maryland `_, + `Universite Paris Saclay `_, + `University of Chinese Academy of Sciences `_, + `The University of Tokyo `_, + `University of Science and Technology of China `_, `University of Southern California `_, `University of Wisconsin Madison `_, `Technical University of Munich `_, @@ -43,6 +51,7 @@ DeepXDE has been used in `University of Colorado Boulder `_, `University of Illinois at Urbana-Champaign `_, `University of California Irvine `_, + `Sun Yat-sen University `_, `King Abdullah University of Science and Technology `_, `University of Oslo `_, `University of Florida `_, @@ -50,8 +59,10 @@ DeepXDE has been used in `University of Exeter `_, `University of Southampton `_, `University of California, Santa Cruz `_, + `University of Padua `_, `Carnegie Mellon University `_, `Seoul National University `_, + `University of Leeds `_, `Sapienza University Rome `_, `University of Alberta `_, `University of Liverpool `_, @@ -74,13 +85,17 @@ DeepXDE has been used in `Southeast University `_, `Delft University of Technology `_, `University of Naples Federico II `_, + `University of Waterloo `_, `Tianjin University `_, `Xiamen University `_, `University of Calgary `_, `Beijing Normal University `_, `Kapodistrian University `_, + `University of Turin `_, `RWTH Aachen University `_, + `National Taiwan University `_, `China University of Geosciences `_, + `Sichuan University `_, `Rice University `_, `Beihang University `_, `University of Sussex `_, @@ -90,18 +105,23 @@ DeepXDE has been used in `Tufts University `_, `Wuhan University of Technology `_, `Universidade do Porto `_, + `University Duisburg-Essen `_, `Florida State University `_, + `Karlsruhe Institute of Technology `_, `University Duisburg-Essen `_, + `Dartmouth College `_, `University of Western Ontario `_, `University of Strasbourg `_, `University of Surrey `_, `Shanghai University `_, `Chalmers University of Technology `_, + `Pontificia Universidad Católica de Chile `_, `Kyushu University `_, `Nagoya University `_, `University of Johannesburg `_, `University of Rome Tor Vergata `_, `University of Kentucky `_, + `Mansoura University `_, `Eindhoven University of Technology `_, `Friedrich Schiller University of Jena `_, `University of Victoria `_, @@ -111,6 +131,8 @@ DeepXDE has been used in `University of Delaware `_, `University of Mississippi `_, `Swansea University `_, + `University of Bath `_, + `University of Trieste `_, `University of the Basque Country `_, `Hong Kong Baptist University `_, `University of Hawaii Manoa `_, @@ -119,15 +141,19 @@ DeepXDE has been used in `University of Sevilla `_, `International School for Advanced Studies `_, `Beijing University of Technology `_, + `Nanjing Tech University `_, `TU Wien `_, `Beijing Jiaotong University `_, + `University of Canterbury `_, `Universidade do Minho `_, `Nanchang University `_, `Carleton University `_, + `Ocean University of China `_, `South China Normal University `_, `Roma Tre University `_, `AmirKabir University of Technology `_, `Sabanci University `_, + `Indian Institute of Technology `_, `Concordia University `_, `Tarbiat Modares University `_, `Graz University of Technology `_, @@ -151,11 +177,15 @@ DeepXDE has been used in `Ulster University `_, `University of Thessaly `_, `Kuwait University `_, + `University of Malaga `_, `Brno University of Technology `_, `Old Dominion University `_, + `Johannes Kepler University Linz `_, `University of Kragujevac `_, `California Polytechnic State University `_, `Chung-Ang University `_, + `Graz University of Technology `_, + `Shahid Beheshti University `_, `Shanghai Normal University `_, `Cadi Ayyad University `_, `Universidad Rey Juan Carlos `_, @@ -166,26 +196,48 @@ DeepXDE has been used in `University of A Coruña `_, `Worcester Polytechnic Institute `_, `Xinjiang University `_, + `LUT University `_, `University of Las Palmas de Gran Canaria `_, + `Nanjing University of Aeronautics and Astronautics ` + `Lahore University of Management Sciences `_, `Hangzhou Dianzi University `_, + `London South Bank University `_, `Taras Shevchenko National University Kiev `_, + `Bundeswehr University Munich `_, `University of Calcutta `_, `University of Kaiserslautern `_, + `Wuhan Textile University `_, `San Francisco State University `_, + `Anhui University of Science and Technology `_, `Boise State University `_, `Necmettin Erbakan University `_, `Shahrekord University `_, + `Shahrood University of Technology `_, + `Yangtze University `_, `Technical University of Cartagena `_, `Adolfo Ibáñez University `_, `Bundeswehr University Munich `_, `Universidad de Burgos `_, + `Shahrood University of Technology `_, `Dong A University `_, + `East China University of Science and Technology Shanghai `_, `Bauhaus-Universität Weimar `_, `Henan Institute of Economics and Trade `_ + `University of the Bundeswehr Munich `_, `National University of Defence Technology `_, `University of Applied Sciences and Arts Northwestern Switzerland `_, `University of Engineering and Management `_, -- > 30 national labs and research institutes, e.g., + `Pontifical Catholic University of Rio de Janeiro `_, + `Fujian Agriculture and Forestry University `_, + `Central South University of Forestry and Technology `_, + `Cho Chun Shik Graduate School of Mobility `_, + `Adama Science and Technology University `_, + `The University of Waikato `_, + `Lishui University `_, + `Westphalian University `_, + `Shanghai Jian Qiao University `_, + `Tel-Aviv University `_, +- > 40 national labs and research institutes, e.g., `Pacific Northwest National Laboratory `_, `Sandia National Laboratories `_, `Argonne National Laboratory `_, @@ -224,6 +276,16 @@ DeepXDE has been used in `Forschungszentrum Jülich `_, `China Ship Scientific Research Center `_, `Yanqi Lake Beijing Institute of Mathematical Sciences and Applications `_ + `Korea Institute of Fusion Energy `_, + `Fraunhofer Heinrich Hertz Institute `_, + `Northwest Institute of Nuclear Technology `_, + `Bay Area Environmental Research Institute `_, + `Lockheed Martin Solar and Astrophysics Laboratory `_, + `CSIRO, Space & Astronomy `_, + `Fujian Special Equipment Inspection and Research Institute `_, + `Centrale Lille Institute `_, + `Science and Technology Facilities Council Scientific Computing Department `_, + `Children’s Hospital of Philadelphia `_, - > 10 industry, e.g., `Anailytica `_, `Ansys `_, @@ -237,13 +299,152 @@ DeepXDE has been used in `Saudi Aramco `_, `Shell `_, `SoftServe `_, - `Quantiph `_ + `Quantiph `_, + `Moldex3D `_ Here is a list of research papers that used DeepXDE. If you would like your paper to appear here, open an issue in the GitHub "Issues" section. PINN ---- +#. L\. Yin & X. Lv. `Adapting physics-informed neural networks for bifurcation detection in ecological migration models `_. *arXiv preprint arXiv:2409.00651*, 2024. +#. K\.-L\. Lu, Y.-M. Su, Z. Bi, C. Qiu, & W.-J. Zhang. `Characteristic performance study on solving oscillator ODEs via soft-constrained physics-informed neural network with small data `_. *arXiv preprint arXiv:2408.11077*, 2024. +#. H\. Gangloff & N. Jouvin. `jinns: a JAX library for physics-informed neural networks `_. *arXiv preprint arXiv:2412.14132*, 2024. +#. M\. J. Choi. `Leveraging turbulence data with physics-informed neural networks `_. *arXiv preprint arXiv:2412.20130*, 2024. +#. P\. Kumar & R. Ranjan. `Evaluation of physics-informed machine learning approach for computation of fluid flows `_. *Proceedings of the 10th International and 50th National Conference on Fluid Mechanics and Fluid Power (FMFP), FMFP2023-FCS-395, December 20–22, IIT Jodhpur, Rajasthan, India*, 2024. +#. K\. Leng, M. Shankar, & J. Thiyagalingam. `Zero coordinate shift: Whetted automatic differentiation for physics-informed neural operators `_. *Journal of Computational Physics*, Volume 505, 112904, 2024. +#. R\. Fang, K. Zhang, K. Song, Y. Kai, Y. Li, & B. Zheng. `A deep learning method for solving thermoelastic coupling problem `_. *Zeitschrift für Naturforschung A*, 79(8), 851–871, 2024. +#. S\. Schoder. `Physics-informed neural networks for modal wave field predictions in 3D room acoustics `_. *Institute of Fundamentals and Theory in Electrical Engineering, Graz University of Technology, Inffeldgasse 18/I, 8010 Graz, Austria*, 2024. +#. L\. Vu-Quoc & A. Humer. `Partial-differential-algebraic equations of nonlinear dynamics by physics-informed neural-network: (I) Operator splitting and framework assessment `_. *Neural Methods in Engineering*, First published: 17 October, 2024. +#. A\. Noorizadegan, R. Cavoretto, D.L. Young, & C.S. Chen. `Stable weight updating: A key to reliable PDE solutions using deep learning `_. *Engineering Analysis with Boundary Elements*, Volume 168, 105933, 2024. +#. C\. Soyarslan & M. Pradas. `Physics-informed machine learning in asymptotic homogenization of elliptic equations `_. *Computer Methods in Applied Mechanics and Engineering*, Volume 427, Part 2, 117043, 2024. +#. A\. Fallah & M.M. Aghdam. `Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation `_. *Engineering with Computers*, 40, 437–454, 2024. +#. Y\. Wu, J. Guo, G. Gopalakrishna, & Z. Poulos. `Deep-MacroFin: Informed equilibrium neural network for continuous time economic models `_. *arXiv preprint arXiv:2408.10368*, 2024. +#. A\. Ogueda-Oliva & P. Seshaiyer. `Literate programming for motivating and teaching neural network-based approaches to solve differential equations `_. *International Journal of Mathematical Education in Science and Technology*, 55(2), 509–542, 2023. +#. A\. T. Deresse & T. T. Dufera. `Exploring physics-informed neural networks for the generalized nonlinear sine-Gordon equation `_. *Applied Computational Intelligence and Soft Computing*, 2024. +#. Y\. Gao, P. Xiao, & Z. Li. `Physics-informed neural networks for solving underwater two-dimensional sound field `_. *2024 OES China Ocean Acoustics (COA)*, pp. 1–4, 2024. +#. J\. Kurz, B. Bowman, M. Seman, et al. `A physics-informed kernel approach to learning the operator for parametric PDEs `_. *Neural Computing and Applications*, 36, 22773–22787, 2024. +#. A\. Newa, A. S. Gearhart, R. A. Darragh, & M. Villafañe-Delgado. `Physics-informed neural networks for scientific modeling: uses, implementations, and directions `_. *Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI*, Vol. 13051, 130511J, 2024. +#. J\. Seo. `Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing `_. *Phys. Rev. E*, 110(2), 025302, 2024. +#. S\. Mtshali, B. A. Jacobs. `Machine learning-based prediction of pharmacokinetic parameters for individualized drug dosage optimization `_. *Int. J. Inf. Tecnol.*, 2024. +#. W\. O. Pedruzzi, C. E. R. Dalla, W. B. D. Silva, D. Guimarães, V. A. Leão, J. C. S. Dutra. `Physics-Informed Neural Network for monitoring the sulfate ion adsorption process using particle filter `_. *An. Acad. Bras. Ciênc.*, 96(4), e20240262, 2024. +#. X\. Wang, M. Sun, Y. Guo, C. Yuan, X. Sun, Z. Wei, X. Jin. `Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data `_. *Journal of Computational Physics*, Volume 502, 112804, 2024. +#. L\. Santos. `Deep and Physics-Informed Neural Networks as a Substitute for Finite Element Analysis `_. *ICMLT '24: Proceedings of the 2024 9th International Conference on Machine Learning Technologies*, Pages 84–90, 2024. +#. R\. C. Sotero, J. M. Sanchez-Bornot, I. Shaharabi-Farahani. `Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks `_. *bioRxiv*, 2024. +#. J\. Song, Z. Yan. `Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning `_. *arXiv preprint arXiv:2409.02339*, 2024. +#. B\. Bhaumik, S. De, S. Changdar. `Deep learning based solution of nonlinear partial differential equations arising in the process of arterial blood flow `_. *Mathematics and Computers in Simulation*, Volume 217, Pages 21–36, 2024. +#. Y\. Tong, S. Xiong, X. He, et al. `RoeNet: Predicting discontinuity of hyperbolic systems from continuous data `_. *Int J Numer Methods Eng*, 125(6), e7406, 2024. +#. H\. Kikumoto, Y. Wang, B. Zhang, H. Jia. `Enhanced Wind Velocity and Pressure Measurement Around Buildings Using Physics-Informed Neural Networks: A Case Study with a Two-Dimensional Urban Street Canyon `_. *Lecture Notes in Civil Engineering*, Volume 553. Springer, Singapore, 2025. +#. C\. B. Ribeiro. `Advanced Numerical Solution of Navier-Stokes Equations with Energy Conservation: A Physics-Informed Neural Networks Approach to Revolutionize Computational Fluid Dynamics `_. December 2024. +#. A\. A. Aghaei, M. M. Moghaddam, K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint arXiv:2409.01899*, 2024. +#. L\. Shang, Y. Zhao, S. Zheng, J. Wang, T. Zhang, J. Wang. `Quantification of gradient energy coefficients using physics-informed neural networks `_. *International Journal of Mechanical Sciences*, Volume 273, 109210, 2024. +#. Z\. Hu, A. Yang, S. Xu, N. Li, Q. Wu, Y. Sun. `Prediction of soliton evolution and parameters evaluation for a high-order nonlinear Schrödinger–Maxwell–Bloch equation in the optical fiber `_. *Physics Letters A*, Volume 531, 130182, 2025. +#. N\. Alzhanov, E. Y. K. Ng, Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. +#. M\. Mircea, D. Garlaschelli, S. Semrau. `Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks `_. *arXiv preprint arXiv:2401.07379*, 2024. +#. D\. Bonnet-Eymard, A. Persoons, M. Faes, D. Moens. `Separable Physics-Informed Neural Networks for Robust Inverse Quantification in Solid Mechanics `_. *International Symposium on Reliability Engineering and Risk Management (ISRERM)*, October 2024. +#. Z\.-Q. Zhang, et al. `Physics-Informed Neural Network Approaches in Quantum Simulations `_. *J. Phys.: Conf. Ser.*, 2891, 062023, 2024. +#. J\. R. Naujoks, A. Krasowski, M. Weckbecker, T. Wiegand, S. Lapuschkin, W. Samek, R. P. Klausen. `PINNfluence: Influence Functions for Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2409.08958*, 2024. +#. C\. J. McDevitt, J. Arnaud, X. Z. Tang. `An Efficient Surrogate Model of Secondary Electron Formation and Evolution `_. *arXiv preprint arXiv:2412.13044*, 2024. +#. L\. Shang, Y. Zhao, S. Zheng, J. Wang, T. Zhang, J. Wang. `Quantification of gradient energy coefficients using physics-informed neural networks `_. *International Journal of Mechanical Sciences*, Volume 273, 109210, 2024. +#. Z\. Hu, A. Yang, S. Xu, N. Li, Q. Wu, Y. Sun. `Prediction of soliton evolution and parameters evaluation for a high-order nonlinear Schrödinger–Maxwell–Bloch equation in the optical fiber `_. *Physics Letters A*, Volume 531, 130182, 2025. +#. N\. Alzhanov, E. Y. K. Ng, Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. +#. M\. Mircea, D. Garlaschelli, S. Semrau. `Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks `_. *arXiv preprint arXiv:2401.07379*, 2024. +#. D\. Bonnet-Eymard, A. Persoons, M. Faes, D. Moens. `Separable Physics-Informed Neural Networks for Robust Inverse Quantification in Solid Mechanics `_. *International Symposium on Reliability Engineering and Risk Management (ISRERM)*, October 2024. +#. Z\.-Q. Zhang, et al. `Physics-Informed Neural Network Approaches in Quantum Simulations `_. *J. Phys.: Conf. Ser.*, 2891, 062023, 2024. +#. J\. R. Naujoks, A. Krasowski, M. Weckbecker, T. Wiegand, S. Lapuschkin, W. Samek, R. P. Klausen. `PINNfluence: Influence Functions for Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2409.08958*, 2024. +#. C\. J. McDevitt, J. Arnaud, X. Z. Tang. `An Efficient Surrogate Model of Secondary Electron Formation and Evolution `_. *arXiv preprint arXiv:2412.13044*, 2024. +#. Z\. Wu, L. J. Jiang, S. Sun, P. Li. `A Hard Constraint and Domain Decomposition Based Physics-Informed Neural Network Framework for Nonhomogeneous Transient Thermal Analysis `_. *IEEE Transactions on Components, Packaging and Manufacturing Technology*, 2024. +#. T\. Sahin, D. Wolff, M. von Danwitz, A. Popp. `Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam `_. *arXiv preprint arXiv:2405.08406*, 2024. +#. S\. Song, H. Jin. `Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks `_. *Soft Matter*, 20(30), 5915–5926, 2024. +#. A\. Ahmad, A. Khan. `Pricing Rainbow Options Using Deep Learning `_. *Preprints*, 2024. +#. P\. Karnakov, S. Litvinov, P. Koumoutsakos. `Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks `_. *PNAS Nexus*, 3(1), pgae005, 2024. +#. T\. Sahin, D. Wolff, M. von Danwitz, A. Popp. `Towards a Hybrid Digital Twin: Fusing Sensor Information and Physics in Surrogate Modeling of a Reinforced Concrete Beam `_. *2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF)*, Bonn, Germany, pp. 1–8, 2024. +#. A\. W. Corrêa do Lago, D. H. Braz de Sousa, P. H. Domingues, M. Daneker, L. Lu, H. V. H. Ayala. `Physics-informed and black-box identification of robotic actuator with a flexible joint `_. *IFAC-PapersOnLine*, 58(15), Pages 259–264, 2024. +#. W\. Hu, S. Zheng, C. Dong, M. Chen, J.-X. Fei, R. Gao. `High-Order Partial Differential Equations Solved by the Improved Self-Adaptive PINNs `_. *SSRN*, 2024. +#. T\. Zou, T. Yajima, Y. Kawajiri. `A parameter estimation method for chromatographic separation process based on physics-informed neural network `_. *Journal of Chromatography A*, Volume 1730, 465077, 2024. +#. H\. Mertens, F. Zhu. `Comparative Analysis of Uncertainty Quantification Models in Active Learning for Efficient System Identification of Dynamical Systems `_. *2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)*, Bari, Italy, pp. 1869–1876, 2024. +#. H\. Zhang, L. Liu, L. Lu. `Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity `_. *arXiv preprint arXiv:2410.13141*, 2024. +#. C\. J. McDevitt, J. Arnaud, X.-Z. Tang. `A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics `_. *arXiv preprint arXiv:2412.12980*, 2024. +#. W\. Quan, X. Ma, Z. Shang, K. Zhao, M. Su, Z. Dong. `Hybrid Physics-Data-Driven Model for Temperature Field Prediction of Asphalt Pavement Based on Physics-Informed Neural Network `_. *SSRN*, 2024. +#. S\. Savović, M. Ivanović, B. Drljača, A. Simović. `Numerical Solution of the Sine–Gordon Equation by Novel Physics-Informed Neural Networks and Two Different Finite Difference Methods `_. *Axioms*, 13(12), 872, 2024. +#. M\. Lamarque, L. Bhan, Y. Shi, M. Krstic. `Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE `_. *arXiv preprint arXiv:2401.07862*, 2024. +#. C\.-E. Chiu, A. Roy, S. Cechnicka, A. Gupta, A. Levy Pinto, C. Galazis, K. Christensen, D. Mandic, M. Varela. `Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions `_. *arXiv preprint arXiv:2409.12712*, 2024. +#. A\. Niewiadomska, et al. `Modeling Tsunami Waves at the Coastline of Valparaiso Area of Chile with Physics Informed Neural Networks `_. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. Lecture Notes in Computer Science, vol 14833. Springer, Cham, 2024. +#. Y\. Chen, H. Yu, C. Liu, J. Xie, J. Han, H. Dai. `Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system `_. *Applied Energy*, Volume 373, 123874, 2024. +#. J\. Hayford, J. Goldman-Wetzler, E. Wang, L. Lu. `Speeding up and reducing memory usage for scientific machine learning via mixed precision `_. *Computer Methods in Applied Mechanics and Engineering*, Volume 428, 117093, 2024. +#. B\. Bhaumik, S. Changdar, S. Chakraverty, S. De. `Effects of viscosity and induced magnetic fields on weakly nonlinear wave transmission in a viscoelastic tube using physics-informed neural networks `_. *Physics of Fluids*, 36(12), 121902, 2024. +#. J\. Li, Y. Lin, K. Zhang. `Dynamic mode decomposition of the core surface flow inverted from geomagnetic field models `_. *Geophysical Research Letters*, 51, e2023GL106362, 2024. +#. T\. Sahin, M. von Danwitz, A. Popp. `Solving forward and inverse problems of contact mechanics using physics-informed neural networks `_. *Advances in Modeling and Simulation in Engineering Sciences*, 11, 11, 2024. +#. V\. Kungurtsev, Y. Peng, J. Gu, S. Vahidian, A. Quinn, F. Idlahcen, Y. Chen. `Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning `_. *arXiv preprint arXiv:2409.01410*, 2024. +#. J\. H. Harmening, F. Pioch, L. Fuhrig, et al. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, 36, 15353–15371, 2024. +#. J\. Duan, H. Zhao, J. Song. `Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics `_. *Journal of the Acoustical Society of America*, 155(5), 3306–3321, 2024. +#. S\. Changdar, B. Bhaumik, N. Sadhukhan, S. Pandey, S. Mukhopadhyay, S. De, S. Bakalis. `A Hybridized Approach on Physics-Informed Neural Networks and Symbolic Regression for Simulating Nonlinear Wave Dynamics in Arterial Blood Flow `_. *SSRN*, 2024. +#. S\. K. Vemuri, T. Büchner, J. Denzler. `Estimating Soil Hydraulic Parameters for Unsaturated Flow Using Physics-Informed Neural Networks `_. *Springer, Cham*, Volume 14834, 2024. +#. W\. Wu, M. Daneker, C. Herz, H. Dewey, J. A. Weiss, A. M. Pouch, L. Lu, M. A. Jolley. `ADEPT: A Noninvasive Method for Determining Elastic Properties of Valve Tissue `_. *arXiv preprint arXiv:2409.19081*, 2024. +#. S\. Changdar, B. Bhaumik, N. Sadhukhan, S. Pandey, S. Mukhopadhyay, S. De, S. Bakalis. `Integrating symbolic regression with physics-informed neural networks for simulating nonlinear wave dynamics in arterial blood flow `_. *Physics of Fluids*, 36(12), 121924, 2024. +#. M\. Y. Hosseini, Y. Shiri. `Flow field reconstruction from sparse sensor measurements with physics-informed neural networks `_. *Physics of Fluids*, 36(7), 073606, 2024. +#. B\. Jang, A. A. Kaptanoglu, R. Gaur, S. Pan, M. Landreman, W. Dorland. `Grad–Shafranov equilibria via data-free physics informed neural networks `_. *Phys. Plasmas*, 31(3), 032510, 2024. +#. H\.-Q. Yang, C. Shi, L. Zhang. `Ensemble learning of soil–water characteristic curve for unsaturated seepage using physics-informed neural networks `_. *Soils and Foundations*, 65(1), 101556, 2025. +#. M\. Peng, H. Tang, Y. Kou. `Adversarial and self-adaptive domain decomposition physics-informed neural networks for high-order differential equations with discontinuities `_. *SSRN*, 2024. +#. H\. Wang, G. Fang, B. Gao, B. Wang, S. Meng. `Inversion of spatially distributed elastic moduli of 2.5D woven composites based on DIC strain field using PINN method `_. *SSRN Electronic Journal*, 2024. +#. L\. Novák, H. Sharma, M. D. Shields. `Physics-informed polynomial chaos expansions `_. *Journal of Computational Physics*, Volume 506, 112926, 2024. +#. J\.-M. Tucny, M. Durve, A. Montessori, S. Succi. `Learning of viscosity functions in rarefied gas flows with physics-informed neural networks `_. *Computers & Fluids*, Volume 269, 106114, 2024. +#. J\.-J. Zhang, N. Cheng, F\.-P. Li, X\.-C. Wang, J\.-N. Chen, L\.-G. Pang, D. Meng. `Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion `_. *arXiv preprint arXiv:2409.06402*, 2024. +#. D\. Sitalo, A. Ogueda-Oliva, P. Seshaiyer. `Data-Driven Mathematical Modeling and Simulation of Migration Dynamics During the Russian-Ukrainian War `_. *Spora: A Journal of Biomathematics*, Vol. 10, 83–90, 2024. +#. J\. Seo. `Solving real-world optimization tasks using physics-informed neural computing `_. *Sci Rep*, 14, 202, 2024. +#. J\. Zhao, Z. Tian, X. Zhang, Z. Duan, J. Lu. `Kinetics Parameter Identification of Chain Shuttling Polymerization Based on Physics-Informed Neural Networks `_. *IFAC-PapersOnLine*, 58(14), 184–191, 2024. +#. K\. Yuan, C. Bauinger, X. Zhang, P. Baehr, M. Kirchhart, D. Dabert, A. Tousnakhoff, P. Boudier, M. Paulitsch. `Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs `_. *arXiv preprint arXiv:2403.17607*, 2024. +#. Z\. Huang, L. An, Y. Ye, X. Wang, H. Cao, Y. Du, M. Zhang. `A broadband modeling method for range-independent underwater acoustic channels using physics-informed neural networks `_. *J. Acoust. Soc. Am.*, 156(5), 3523–3533, 2024. +#. P\. Xiao, M. Zheng, A. Jiao, X. Yang, L. Lu. `Quantum DeepONet: Neural operators accelerated by quantum computing `_. *arXiv preprint arXiv:2409.15683*, 2024. +#. Y\. Yang, P. He, X. Peng, Q. He. `A number-theoretic method sampling neural network for solving partial differential equations `_. *arXiv preprint arXiv:2411.17039*, 2025. +#. J\. Cho, S. Nam, H. Yang, S\.-B. Yun, Y. Hong, E. Park. `Separable Physics-Informed Neural Networks `_. *Advances in Neural Information Processing Systems*, 36, 23761–23788, 2023. +#. C\. Galazis, C\.-E. Chiu, T. Arichi, A\. A. Bharath, M. Varela. `PINNing Cerebral Blood Flow: Analysis of Perfusion MRI in Infants using Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2410.19759*, 2024. +#. W\. Hu. `A new method to solve the forward and inverse problems for the spatial Solow model by using Physics Informed Neural Networks (PINNs) `_. *Engineering Analysis with Boundary Elements*, 169(Part B), 106013, 2024. +#. X\. Wang, C. Luo, D. Jiang, H. Wang, Z. Wang. `Improved design method for gas carburizing process through data-driven and physical information `_. *Computational Materials Science*, Volume 247, 113507, 2025. +#. M\. Xie, X. Zhao, D. Zhao, J. Fu, C. Shelton, B. Semlitsch. `Predicting bifurcation and amplitude death characteristics of thermoacoustic instabilities from PINNs-derived van der Pol oscillators `_. *Journal of Fluid Mechanics*, 998, A46, 2024. +#. A\. Serebrennikova, R. Teubler, L. Hoffellner, E. Leitner, U. Hirn, K. Zojer. `Physics informed neural networks reveal valid models for reactive diffusion of volatiles through paper `_. *Chemical Engineering Science*, Volume 285, 119636, 2024. +#. C\.A. Molina Catricheo, F. Lambert, J. Salomon, et al. `Modeling global surface dust deposition using physics-informed neural networks `_. *Communications Earth & Environment*, 5, 778, 2024. +#. N\. Patel, A. Aykutalp, P. Laguna. `Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks `_. *arXiv preprint arXiv:2401.01440*, 2024. +#. A\. Deresse, T. Dufera. `A deep learning approach: Physics-informed neural networks for solving the 2D nonlinear Sine–Gordon equation `_. *Results in Applied Mathematics*, 25, 2024. +#. C\. Kou, Y. Yin, Y. Zeng, S. Jia, Y. Luo, X. Yuan. `Physics-informed neural network integrate with unclosed mechanism model for turbulent mass transfer `_. *Chemical Engineering Science*, Volume 288, 119752, 2024. +#. N\. Patel, A. Aykutalp, P. Laguna. `Novel approach to solving Schwarzschild black hole perturbation equations via physics informed neural networks `_. *Gen Relativ Gravit*, 56, 137, 2024. +#. Z\. Zhang, J.-H. Lee, L. Sun, G. X. Gu. `Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials `_. *PNAS Nexus*, 3(5), pgae186, May 2024. +#. A\. Jesser, K. Krycki, R. G. McClarren, & M. Frank. `Numerical Robustness of PINNs for Multiscale Transport Equations `_. *arXiv preprint arXiv:2412.14683*, 2024. +#. H\. Wu, H. Luo, Y. Ma, J. Wang, & M. Long. `RoPINN: Region Optimized Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2405.14369*, 2024. +#. Y\. Zhao, Y. Fei, R. P. Singh, & D. Fu. `Experimental and Numerical Simulation of the High Hydrological Performance of Root-Zone Mixture in Sports Turf `_. *SSRN*, 2024. +#. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. Garcia de Soto, T. Abdoun, & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint arXiv:2409.00994*, 2024. +#. H\. Wang, Y. Pu, S. Song, & G. Huang. `Physics-informed Dynamics Representation Learning for Parametric PDEs `_. *OpenReview*, 2024. +#. R\. Casado-Vara, M. Severt, A. Díaz-Longueira, Á. M. Rey, & J. L. Calvo-Rolle. `Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach `_. *Mathematics*, 12(2), 250, 2024. +#. E\. Raeisi, M. Yavuz, M. Khosravifarsani, Y. Fadaei. `Mathematical modeling of interactions between colon cancer and immune system with a deep learning algorithm `_. *Eur. Phys. J. Plus*, 139(4), 345, 2024. +#. J\. Song & Z. Yan. `Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning `_. *arXiv preprint*, arXiv:2409.02339, 2024. +#. J\. J. Athalathil, B. Vaidya, S. Kundu, V. Upendran & M. C. M. Cheung. `Surface Flux Transport Modeling Using Physics-informed Neural Networks `_. *The Astrophysical Journal*, 975(2), 258, 2024. +#. A\. A. Aghaei, M. M. Moghaddam & K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint*, arXiv:2409.01899, 2024. +#. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. G. de Soto, T. Abdoun & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint*, arXiv:2409.00994, 2024. +#. L\. Yin & X. Lv. `Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models `_. *arXiv preprint*, arXiv:2409.00651, 2024. +#. L\. Shang, S. Zheng, J. Wang & J. Wang. `Physics-informed neural networks incorporating energy dissipation for the phase-field model of ferroelectric microstructure evolution `_. *arXiv preprint*, arXiv:2409.02959, 2024. +#. K\.-L. Lu, Y\.-M. Su, C. Qiu, Z. Bi & W\.-J. Zhang. `Solving Oscillator ODEs via Soft-constrained Physics-informed Neural Network with Small Data `_. *arXiv e-prints*, arXiv:2408, 2024. +#. Y\. Wu, J. Guo, G. Gopalakrishna & Z. Poulos. `Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models `_. *arXiv preprint*, arXiv:2408.10368, 2024. +#. J\. Seo. `Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing `_. *Physical Review E*, 110(2), 025302, 2024. +#. L\. Vu-Quoc & A. Humer. `Partial-differential-algebraic equations of nonlinear dynamics by physics-informed neural-network: (I) Operator splitting and framework assessment `_. *International Journal for Numerical Methods in Engineering*, 125(24), e7586, 2024. +#. Z\. Xiong, Y. Jiang, W. Lu, X. Wang & T. Tian. `Reconstructing and Forecasting Marine Dynamic Variable Fields across Space and Time Globally and Gaplessly `_. *arXiv preprint*, arXiv:2408.01509, 2024. +#. J\. H. Adler, S. Hocking, X. Hu & S. Islam. `Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems `_. *arXiv preprint*, arXiv:2407.18057, 2024. +#. H\. Kikumoto, Y. Wang, B. Zhang & H. Jia. `Enhanced Wind Velocity and Pressure Measurement Around Buildings Using Physics-Informed Neural Networks: A Case Study with a Two-Dimensional Urban Street Canyon `_. *International Association of Building Physics*, pp. 390-396, 2024. +#. Y\. Chen, H. Yu, C. Liu, J. Xie, J. Han & H. Dai. `Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system `_. *Applied Energy*, 373, 123874, 2024. +#. A\. Noorizadegan, R. Cavoretto, D. L. Young & C. S. Chen. `Stable weight updating: A key to reliable PDE solutions using deep learning `_. *Engineering Analysis with Boundary Elements*, 168, 105933, 2024. +#. D\. Nguyen. `Advanced modeling of the childbirth system using different deep learning methods: from fetal skeleton segmentation to real-time soft tissue deformation `_. PhD thesis, Centrale Lille Institut, 2024. +#. A\. Jiao, Q. Yan, J. Harlim & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint*, arXiv:2407.05477, 2024. +#. M\. Y. Hosseini & Y. Shiri. `Flow field reconstruction from sparse sensor measurements with physics-informed neural networks `_. *Physics of Fluids*, 36(7), 2024. +#. N\. Alzhanov, E. Y. K. Ng & Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. +#. S\. Sripada, A. U. Gaitonde, J. A. Weibel & A. M. Marconnet. `Robust inverse parameter fitting of thermal properties from the laser-based Ångstrom method in the presence of measurement noise using physics-informed neural networks (PINNs) `_. *Journal of Applied Physics*, 135(22), 2024. +#. T\. Zou, T. Yajima & Y. Kawajiri. `A parameter estimation method for chromatographic separation process based on physics-informed neural network `_. *Journal of Chromatography A*, 465077, 2024. +#. H\. Wang, G. Fang, B. Gao, B. Wang & S. Meng. `Inversion of Spatially Distributed Elastic Moduli of 2.5D Woven Composites Based on DIC Strain Field Using PINN Method `_. *SSRN preprint*, 4851306, 2024. +#. H\. Lu, Q. Wang, W. Tang & H. Liu. `Physics-informed neural networks for fully non-linear free surface wave propagation `_. *Physics of Fluids*, 36(6), 2024. +#. N\. Jha & E. Mallik. `GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point Boundary Value Problems `_. *Neural Processing Letters*, 56(3), 192, 2024. +#. Y\. Gao, P. Xiao & Z. Li. `Physics-Informed Neural Networks for Solving Underwater Two-dimensional Sound Field `_. *2024 OES China Ocean Acoustics (COA)*, pp. 1-4, IEEE, 2024. +#. N\. Jha & E. Mallik. `Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs `_. *Physica Scripta*, 2024. +#. R\. Fang, K. Zhang, K. Song, Y. Kai, Y. Li & B. Zheng. `A deep learning method for solving thermoelastic coupling problem `_. *Zeitschrift für Naturforschung A*, 2024. +#. J\. H. Harmening, F. Pioch, L. Fuhrig, F.-J. Peitzmann, D. Schramm & O. el Moctar. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, pp. 1-19, 2024. +#. T\. Sahin, D. Wolff, M. von Danwitz & A. Popp. `Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam `_. *arXiv preprint*, arXiv:2405.08406, 2024. #. S\. K. Vemuri, T. Büchner, & J. Denzler. `Estimating soil hydraulic parameters for unsaturated flow using physics-informed neural networks `_. In *International Conference on Computational Science*, 338-351, Cham: Springer Nature Switzerland, 2024, June. #. N\. A. Niewiadomska, P. Maczuga, A. Oliver-Serra, L. Siwik, P. Sepulveda-Salaz, A. Paszyńska, M. Paszyński, & K. Pingali. `Modeling tsunami waves at the coastline of Valparaiso area of Chile with physics informed neural networks `_. In *International Conference on Computational Science*, 204-218, Cham: Springer Nature Switzerland, 2024, June. #. N\. Alzhanov, E. Y. K. Ng, & Y. Zhao. `Three-dimensional physics-informed neural network simulation in coronary artery trees `_. *Fluids*, 9(7), 2024. @@ -465,6 +666,24 @@ PINN Deep neural operators -------- +#. J\. He, D. Pal, A. Najafi, et al. `Material-Response-Informed DeepONet and Its Application to Polycrystal Stress–Strain Prediction in Crystal Plasticity `_. *JOM*, 76, 5744–5754, 2024. +#. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477*, 2024. +#. J\. Park, & N. Kang. `Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions `_. *arXiv preprint arXiv:2412.18362*, 2024. +#. K\. Lv, J. Wang, Y. Zhang, & H. Yu. `Neural Operators for Adaptive Control of Freeway Traffic `_. *arXiv preprint arXiv:2410.20708*, 2024. +#. Z\. Li, H. Zheng, N. Kovachki, D. Jin, H. Chen, B. Liu, K. Azizzadenesheli, & A. Anandkumar. `Physics-Informed Neural Operator for Learning Partial Differential Equations `_. *Association for Computing Machinery*, 1(3), September 2024. +#. C\. García-Cervera, M. Kessler, P. Pedregal, & F. Periago. `Universal approximation of set-valued maps and DeepONet approximation of the controllability map `_. *ResearchGate*, December 2024. +#. J\. He, S. Koric, D. Abueidda, A. Najafi, & I. 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Lu. `Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration `_. *Reliability Engineering & System Safety*, Volume 251, 110392, 2024. +#. Q\. Meng, Y. Li, Z. Deng, X. Liu, G. Chen, Q. Wu, C. Liu, & X. Hao. `A general reduced-order neural operator for spatio-temporal predictive learning on complex spatial domains `_. *arXiv preprint arXiv:2409.05508*, 2024. +#. A\. A. Aghaei, M. M. Moghaddam, & K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint arXiv:2409.01899*, 2024. +#. K\. Lv, J. Wang, & Y. Cao. `Neural Operator Approximations for Boundary Stabilization of Cascaded Parabolic PDEs `_. *International Journal of Adaptive Control and Signal Processing*, Wiley Online Library, 2024. +#. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. G. de Soto, T. Abdoun, & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint arXiv:2409.00994*, 2024. +#. P\. Gao, G. E. Karniadakis, & P. Stinis. `Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks `_. *arXiv preprint arXiv:2408.03263*, 2024. +#. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477*, 2024. +#. L\. Xiao, G. Mei, & N. Xu. `Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region `_. *Journal of Rock Mechanics and Geotechnical Engineering*, Elsevier, 2024. #. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477v1*, 2024. #. G\. Fabiani, I. G. Kevrekidis, C. Siettos, & A. N. Yannacopoulos. `RandONet: Shallow-networks with random projections for learning linear and nonlinear operators `_. *Computer Methods in Applied Mechanics and Engineering*, 429:117130, 2024. #. A\. Jiao, H. He, R. Ranade, J. Pathak, & L. Lu. `One-shot learning for solution operators of partial differential equations `_. *arXiv preprint arXiv:2104.05512*, 2024. From fc6dadfc12beab397fddd16918bee202aa1ab0fd Mon Sep 17 00:00:00 2001 From: KangyuWeng Date: Mon, 20 Jan 2025 21:05:20 -0500 Subject: [PATCH 2/4] Check for duplicated papers --- docs/user/research.rst | 109 +++++------------------------------------ 1 file changed, 12 insertions(+), 97 deletions(-) diff --git a/docs/user/research.rst b/docs/user/research.rst index 635ad2f77..45e0cf5a1 100644 --- a/docs/user/research.rst +++ b/docs/user/research.rst @@ -39,7 +39,6 @@ DeepXDE has been used in `Boston University `_, `University of Maryland `_, `Universite Paris Saclay `_, - `University of Chinese Academy of Sciences `_, `The University of Tokyo `_, `University of Science and Technology of China `_, `University of Southern California `_, @@ -105,7 +104,6 @@ DeepXDE has been used in `Tufts University `_, `Wuhan University of Technology `_, `Universidade do Porto `_, - `University Duisburg-Essen `_, `Florida State University `_, `Karlsruhe Institute of Technology `_, `University Duisburg-Essen `_, @@ -156,7 +154,6 @@ DeepXDE has been used in `Indian Institute of Technology `_, `Concordia University `_, `Tarbiat Modares University `_, - `Graz University of Technology `_, `National University of Colombia `_, `Clemson University `_, `Dortmund University of Technology `_, @@ -203,7 +200,6 @@ DeepXDE has been used in `Hangzhou Dianzi University `_, `London South Bank University `_, `Taras Shevchenko National University Kiev `_, - `Bundeswehr University Munich `_, `University of Calcutta `_, `University of Kaiserslautern `_, `Wuhan Textile University `_, @@ -218,12 +214,10 @@ DeepXDE has been used in `Adolfo Ibáñez University `_, `Bundeswehr University Munich `_, `Universidad de Burgos `_, - `Shahrood University of Technology `_, `Dong A University `_, `East China University of Science and Technology Shanghai `_, `Bauhaus-Universität Weimar `_, `Henan Institute of Economics and Trade `_ - `University of the Bundeswehr Munich `_, `National University of Defence Technology `_, `University of Applied Sciences and Arts Northwestern Switzerland `_, `University of Engineering and Management `_, @@ -318,11 +312,7 @@ PINN #. L\. Vu-Quoc & A. Humer. `Partial-differential-algebraic equations of nonlinear dynamics by physics-informed neural-network: (I) Operator splitting and framework assessment `_. *Neural Methods in Engineering*, First published: 17 October, 2024. #. A\. Noorizadegan, R. Cavoretto, D.L. Young, & C.S. Chen. `Stable weight updating: A key to reliable PDE solutions using deep learning `_. *Engineering Analysis with Boundary Elements*, Volume 168, 105933, 2024. #. C\. Soyarslan & M. Pradas. `Physics-informed machine learning in asymptotic homogenization of elliptic equations `_. *Computer Methods in Applied Mechanics and Engineering*, Volume 427, Part 2, 117043, 2024. -#. A\. Fallah & M.M. Aghdam. `Physics-informed neural network for bending and free vibration analysis of three-dimensional functionally graded porous beam resting on elastic foundation `_. *Engineering with Computers*, 40, 437–454, 2024. #. Y\. Wu, J. Guo, G. Gopalakrishna, & Z. Poulos. `Deep-MacroFin: Informed equilibrium neural network for continuous time economic models `_. *arXiv preprint arXiv:2408.10368*, 2024. -#. A\. Ogueda-Oliva & P. Seshaiyer. `Literate programming for motivating and teaching neural network-based approaches to solve differential equations `_. *International Journal of Mathematical Education in Science and Technology*, 55(2), 509–542, 2023. -#. A\. T. Deresse & T. T. Dufera. `Exploring physics-informed neural networks for the generalized nonlinear sine-Gordon equation `_. *Applied Computational Intelligence and Soft Computing*, 2024. -#. Y\. Gao, P. Xiao, & Z. Li. `Physics-informed neural networks for solving underwater two-dimensional sound field `_. *2024 OES China Ocean Acoustics (COA)*, pp. 1–4, 2024. #. J\. Kurz, B. Bowman, M. Seman, et al. `A physics-informed kernel approach to learning the operator for parametric PDEs `_. *Neural Computing and Applications*, 36, 22773–22787, 2024. #. A\. Newa, A. S. Gearhart, R. A. Darragh, & M. Villafañe-Delgado. `Physics-informed neural networks for scientific modeling: uses, implementations, and directions `_. *Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI*, Vol. 13051, 130511J, 2024. #. J\. Seo. `Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing `_. *Phys. Rev. E*, 110(2), 025302, 2024. @@ -330,68 +320,40 @@ PINN #. W\. O. Pedruzzi, C. E. R. Dalla, W. B. D. Silva, D. Guimarães, V. A. Leão, J. C. S. Dutra. `Physics-Informed Neural Network for monitoring the sulfate ion adsorption process using particle filter `_. *An. Acad. Bras. Ciênc.*, 96(4), e20240262, 2024. #. X\. Wang, M. Sun, Y. Guo, C. Yuan, X. Sun, Z. Wei, X. Jin. `Octree-based hierarchical sampling optimization for the volumetric super-resolution of scientific data `_. *Journal of Computational Physics*, Volume 502, 112804, 2024. #. L\. Santos. `Deep and Physics-Informed Neural Networks as a Substitute for Finite Element Analysis `_. *ICMLT '24: Proceedings of the 2024 9th International Conference on Machine Learning Technologies*, Pages 84–90, 2024. -#. R\. C. Sotero, J. M. Sanchez-Bornot, I. Shaharabi-Farahani. `Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks `_. *bioRxiv*, 2024. -#. J\. Song, Z. Yan. `Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning `_. *arXiv preprint arXiv:2409.02339*, 2024. -#. B\. Bhaumik, S. De, S. Changdar. `Deep learning based solution of nonlinear partial differential equations arising in the process of arterial blood flow `_. *Mathematics and Computers in Simulation*, Volume 217, Pages 21–36, 2024. #. Y\. Tong, S. Xiong, X. He, et al. `RoeNet: Predicting discontinuity of hyperbolic systems from continuous data `_. *Int J Numer Methods Eng*, 125(6), e7406, 2024. #. H\. Kikumoto, Y. Wang, B. Zhang, H. Jia. `Enhanced Wind Velocity and Pressure Measurement Around Buildings Using Physics-Informed Neural Networks: A Case Study with a Two-Dimensional Urban Street Canyon `_. *Lecture Notes in Civil Engineering*, Volume 553. Springer, Singapore, 2025. #. C\. B. Ribeiro. `Advanced Numerical Solution of Navier-Stokes Equations with Energy Conservation: A Physics-Informed Neural Networks Approach to Revolutionize Computational Fluid Dynamics `_. December 2024. -#. A\. A. Aghaei, M. M. Moghaddam, K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint arXiv:2409.01899*, 2024. #. L\. Shang, Y. Zhao, S. Zheng, J. Wang, T. Zhang, J. Wang. `Quantification of gradient energy coefficients using physics-informed neural networks `_. *International Journal of Mechanical Sciences*, Volume 273, 109210, 2024. #. Z\. Hu, A. Yang, S. Xu, N. Li, Q. Wu, Y. Sun. `Prediction of soliton evolution and parameters evaluation for a high-order nonlinear Schrödinger–Maxwell–Bloch equation in the optical fiber `_. *Physics Letters A*, Volume 531, 130182, 2025. -#. N\. Alzhanov, E. Y. K. Ng, Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. -#. M\. Mircea, D. Garlaschelli, S. Semrau. `Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks `_. *arXiv preprint arXiv:2401.07379*, 2024. -#. D\. Bonnet-Eymard, A. Persoons, M. Faes, D. Moens. `Separable Physics-Informed Neural Networks for Robust Inverse Quantification in Solid Mechanics `_. *International Symposium on Reliability Engineering and Risk Management (ISRERM)*, October 2024. -#. Z\.-Q. Zhang, et al. `Physics-Informed Neural Network Approaches in Quantum Simulations `_. *J. Phys.: Conf. Ser.*, 2891, 062023, 2024. -#. J\. R. Naujoks, A. Krasowski, M. Weckbecker, T. Wiegand, S. Lapuschkin, W. Samek, R. P. Klausen. `PINNfluence: Influence Functions for Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2409.08958*, 2024. -#. C\. J. McDevitt, J. Arnaud, X. Z. Tang. `An Efficient Surrogate Model of Secondary Electron Formation and Evolution `_. *arXiv preprint arXiv:2412.13044*, 2024. -#. L\. Shang, Y. Zhao, S. Zheng, J. Wang, T. Zhang, J. Wang. `Quantification of gradient energy coefficients using physics-informed neural networks `_. *International Journal of Mechanical Sciences*, Volume 273, 109210, 2024. -#. Z\. Hu, A. Yang, S. Xu, N. Li, Q. Wu, Y. Sun. `Prediction of soliton evolution and parameters evaluation for a high-order nonlinear Schrödinger–Maxwell–Bloch equation in the optical fiber `_. *Physics Letters A*, Volume 531, 130182, 2025. -#. N\. Alzhanov, E. Y. K. Ng, Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. -#. M\. Mircea, D. Garlaschelli, S. Semrau. `Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks `_. *arXiv preprint arXiv:2401.07379*, 2024. #. D\. Bonnet-Eymard, A. Persoons, M. Faes, D. Moens. `Separable Physics-Informed Neural Networks for Robust Inverse Quantification in Solid Mechanics `_. *International Symposium on Reliability Engineering and Risk Management (ISRERM)*, October 2024. #. Z\.-Q. Zhang, et al. `Physics-Informed Neural Network Approaches in Quantum Simulations `_. *J. Phys.: Conf. Ser.*, 2891, 062023, 2024. #. J\. R. Naujoks, A. Krasowski, M. Weckbecker, T. 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Kawajiri. `A parameter estimation method for chromatographic separation process based on physics-informed neural network `_. *Journal of Chromatography A*, Volume 1730, 465077, 2024. #. H\. Mertens, F. Zhu. `Comparative Analysis of Uncertainty Quantification Models in Active Learning for Efficient System Identification of Dynamical Systems `_. *2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)*, Bari, Italy, pp. 1869–1876, 2024. #. H\. Zhang, L. Liu, L. Lu. `Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity `_. *arXiv preprint arXiv:2410.13141*, 2024. #. C\. J. McDevitt, J. Arnaud, X.-Z. Tang. `A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics `_. *arXiv preprint arXiv:2412.12980*, 2024. #. W\. Quan, X. Ma, Z. Shang, K. Zhao, M. Su, Z. Dong. `Hybrid Physics-Data-Driven Model for Temperature Field Prediction of Asphalt Pavement Based on Physics-Informed Neural Network `_. *SSRN*, 2024. #. S\. Savović, M. Ivanović, B. Drljača, A. Simović. `Numerical Solution of the Sine–Gordon Equation by Novel Physics-Informed Neural Networks and Two Different Finite Difference Methods `_. *Axioms*, 13(12), 872, 2024. -#. M\. Lamarque, L. Bhan, Y. Shi, M. Krstic. `Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE `_. *arXiv preprint arXiv:2401.07862*, 2024. #. C\.-E. Chiu, A. Roy, S. Cechnicka, A. Gupta, A. Levy Pinto, C. Galazis, K. Christensen, D. Mandic, M. Varela. `Physics-Informed Neural Networks can accurately model cardiac electrophysiology in 3D geometries and fibrillatory conditions `_. *arXiv preprint arXiv:2409.12712*, 2024. -#. A\. Niewiadomska, et al. `Modeling Tsunami Waves at the Coastline of Valparaiso Area of Chile with Physics Informed Neural Networks `_. 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Zhang. `Dynamic mode decomposition of the core surface flow inverted from geomagnetic field models `_. *Geophysical Research Letters*, 51, e2023GL106362, 2024. #. T\. Sahin, M. von Danwitz, A. Popp. `Solving forward and inverse problems of contact mechanics using physics-informed neural networks `_. *Advances in Modeling and Simulation in Engineering Sciences*, 11, 11, 2024. #. V\. Kungurtsev, Y. Peng, J. Gu, S. Vahidian, A. Quinn, F. Idlahcen, Y. Chen. `Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning `_. *arXiv preprint arXiv:2409.01410*, 2024. -#. J\. H. Harmening, F. Pioch, L. Fuhrig, et al. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, 36, 15353–15371, 2024. #. J\. Duan, H. Zhao, J. Song. `Spatial domain decomposition-based physics-informed neural networks for practical acoustic propagation estimation under ocean dynamics `_. *Journal of the Acoustical Society of America*, 155(5), 3306–3321, 2024. #. S\. Changdar, B. Bhaumik, N. Sadhukhan, S. Pandey, S. Mukhopadhyay, S. De, S. Bakalis. `A Hybridized Approach on Physics-Informed Neural Networks and Symbolic Regression for Simulating Nonlinear Wave Dynamics in Arterial Blood Flow `_. *SSRN*, 2024. -#. S\. K. Vemuri, T. Büchner, J. Denzler. `Estimating Soil Hydraulic Parameters for Unsaturated Flow Using Physics-Informed Neural Networks `_. *Springer, Cham*, Volume 14834, 2024. #. W\. Wu, M. Daneker, C. Herz, H. Dewey, J. A. Weiss, A. M. Pouch, L. Lu, M. A. Jolley. `ADEPT: A Noninvasive Method for Determining Elastic Properties of Valve Tissue `_. *arXiv preprint arXiv:2409.19081*, 2024. #. S\. Changdar, B. Bhaumik, N. Sadhukhan, S. Pandey, S. Mukhopadhyay, S. De, S. Bakalis. `Integrating symbolic regression with physics-informed neural networks for simulating nonlinear wave dynamics in arterial blood flow `_. *Physics of Fluids*, 36(12), 121924, 2024. -#. M\. Y. Hosseini, Y. Shiri. `Flow field reconstruction from sparse sensor measurements with physics-informed neural networks `_. *Physics of Fluids*, 36(7), 073606, 2024. -#. B\. Jang, A. A. Kaptanoglu, R. Gaur, S. Pan, M. Landreman, W. Dorland. `Grad–Shafranov equilibria via data-free physics informed neural networks `_. *Phys. Plasmas*, 31(3), 032510, 2024. #. H\.-Q. Yang, C. Shi, L. Zhang. `Ensemble learning of soil–water characteristic curve for unsaturated seepage using physics-informed neural networks `_. *Soils and Foundations*, 65(1), 101556, 2025. #. M\. Peng, H. Tang, Y. Kou. `Adversarial and self-adaptive domain decomposition physics-informed neural networks for high-order differential equations with discontinuities `_. *SSRN*, 2024. #. H\. Wang, G. Fang, B. Gao, B. Wang, S. Meng. `Inversion of spatially distributed elastic moduli of 2.5D woven composites based on DIC strain field using PINN method `_. *SSRN Electronic Journal*, 2024. #. L\. Novák, H. Sharma, M. D. Shields. `Physics-informed polynomial chaos expansions `_. *Journal of Computational Physics*, Volume 506, 112926, 2024. -#. J\.-M. Tucny, M. Durve, A. Montessori, S. Succi. `Learning of viscosity functions in rarefied gas flows with physics-informed neural networks `_. *Computers & Fluids*, Volume 269, 106114, 2024. #. J\.-J. Zhang, N. Cheng, F\.-P. Li, X\.-C. Wang, J\.-N. Chen, L\.-G. Pang, D. Meng. `Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion `_. *arXiv preprint arXiv:2409.06402*, 2024. #. D\. Sitalo, A. Ogueda-Oliva, P. Seshaiyer. `Data-Driven Mathematical Modeling and Simulation of Migration Dynamics During the Russian-Ukrainian War `_. *Spora: A Journal of Biomathematics*, Vol. 10, 83–90, 2024. -#. J\. Seo. `Solving real-world optimization tasks using physics-informed neural computing `_. *Sci Rep*, 14, 202, 2024. #. J\. Zhao, Z. Tian, X. Zhang, Z. Duan, J. Lu. `Kinetics Parameter Identification of Chain Shuttling Polymerization Based on Physics-Informed Neural Networks `_. *IFAC-PapersOnLine*, 58(14), 184–191, 2024. #. K\. Yuan, C. Bauinger, X. Zhang, P. Baehr, M. Kirchhart, D. Dabert, A. Tousnakhoff, P. Boudier, M. Paulitsch. `Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs `_. *arXiv preprint arXiv:2403.17607*, 2024. #. Z\. Huang, L. An, Y. Ye, X. Wang, H. Cao, Y. Du, M. Zhang. `A broadband modeling method for range-independent underwater acoustic channels using physics-informed neural networks `_. *J. Acoust. Soc. Am.*, 156(5), 3523–3533, 2024. @@ -404,68 +366,41 @@ PINN #. M\. Xie, X. Zhao, D. Zhao, J. Fu, C. Shelton, B. Semlitsch. `Predicting bifurcation and amplitude death characteristics of thermoacoustic instabilities from PINNs-derived van der Pol oscillators `_. *Journal of Fluid Mechanics*, 998, A46, 2024. #. A\. Serebrennikova, R. Teubler, L. Hoffellner, E. Leitner, U. Hirn, K. Zojer. `Physics informed neural networks reveal valid models for reactive diffusion of volatiles through paper `_. *Chemical Engineering Science*, Volume 285, 119636, 2024. #. C\.A. Molina Catricheo, F. Lambert, J. Salomon, et al. `Modeling global surface dust deposition using physics-informed neural networks `_. *Communications Earth & Environment*, 5, 778, 2024. -#. N\. Patel, A. Aykutalp, P. Laguna. `Calculating Quasi-Normal Modes of Schwarzschild Black Holes with Physics Informed Neural Networks `_. *arXiv preprint arXiv:2401.01440*, 2024. #. A\. Deresse, T. Dufera. `A deep learning approach: Physics-informed neural networks for solving the 2D nonlinear Sine–Gordon equation `_. *Results in Applied Mathematics*, 25, 2024. -#. C\. Kou, Y. Yin, Y. Zeng, S. Jia, Y. Luo, X. Yuan. `Physics-informed neural network integrate with unclosed mechanism model for turbulent mass transfer `_. *Chemical Engineering Science*, Volume 288, 119752, 2024. #. N\. Patel, A. Aykutalp, P. Laguna. `Novel approach to solving Schwarzschild black hole perturbation equations via physics informed neural networks `_. *Gen Relativ Gravit*, 56, 137, 2024. -#. Z\. Zhang, J.-H. Lee, L. Sun, G. X. Gu. `Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials `_. *PNAS Nexus*, 3(5), pgae186, May 2024. #. A\. Jesser, K. Krycki, R. G. McClarren, & M. Frank. `Numerical Robustness of PINNs for Multiscale Transport Equations `_. *arXiv preprint arXiv:2412.14683*, 2024. #. H\. Wu, H. Luo, Y. Ma, J. Wang, & M. Long. `RoPINN: Region Optimized Physics-Informed Neural Networks `_. *arXiv preprint arXiv:2405.14369*, 2024. #. Y\. Zhao, Y. Fei, R. P. Singh, & D. Fu. `Experimental and Numerical Simulation of the High Hydrological Performance of Root-Zone Mixture in Sports Turf `_. *SSRN*, 2024. -#. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. Garcia de Soto, T. Abdoun, & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint arXiv:2409.00994*, 2024. #. H\. Wang, Y. Pu, S. Song, & G. Huang. `Physics-informed Dynamics Representation Learning for Parametric PDEs `_. *OpenReview*, 2024. -#. R\. Casado-Vara, M. Severt, A. Díaz-Longueira, Á. M. Rey, & J. L. Calvo-Rolle. `Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach `_. *Mathematics*, 12(2), 250, 2024. -#. E\. Raeisi, M. Yavuz, M. Khosravifarsani, Y. Fadaei. `Mathematical modeling of interactions between colon cancer and immune system with a deep learning algorithm `_. *Eur. Phys. J. Plus*, 139(4), 345, 2024. #. J\. Song & Z. Yan. `Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning `_. *arXiv preprint*, arXiv:2409.02339, 2024. #. J\. J. Athalathil, B. Vaidya, S. Kundu, V. Upendran & M. C. M. Cheung. `Surface Flux Transport Modeling Using Physics-informed Neural Networks `_. *The Astrophysical Journal*, 975(2), 258, 2024. #. A\. A. Aghaei, M. M. Moghaddam & K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint*, arXiv:2409.01899, 2024. -#. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. G. de Soto, T. Abdoun & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint*, arXiv:2409.00994, 2024. -#. L\. Yin & X. Lv. `Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models `_. *arXiv preprint*, arXiv:2409.00651, 2024. #. L\. Shang, S. Zheng, J. Wang & J. Wang. `Physics-informed neural networks incorporating energy dissipation for the phase-field model of ferroelectric microstructure evolution `_. *arXiv preprint*, arXiv:2409.02959, 2024. #. K\.-L. Lu, Y\.-M. Su, C. Qiu, Z. Bi & W\.-J. Zhang. `Solving Oscillator ODEs via Soft-constrained Physics-informed Neural Network with Small Data `_. *arXiv e-prints*, arXiv:2408, 2024. -#. Y\. Wu, J. Guo, G. Gopalakrishna & Z. Poulos. `Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models `_. *arXiv preprint*, arXiv:2408.10368, 2024. -#. J\. Seo. `Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing `_. *Physical Review E*, 110(2), 025302, 2024. -#. L\. Vu-Quoc & A. Humer. `Partial-differential-algebraic equations of nonlinear dynamics by physics-informed neural-network: (I) Operator splitting and framework assessment `_. *International Journal for Numerical Methods in Engineering*, 125(24), e7586, 2024. #. Z\. Xiong, Y. Jiang, W. Lu, X. Wang & T. Tian. `Reconstructing and Forecasting Marine Dynamic Variable Fields across Space and Time Globally and Gaplessly `_. *arXiv preprint*, arXiv:2408.01509, 2024. #. J\. H. Adler, S. Hocking, X. Hu & S. Islam. `Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems `_. *arXiv preprint*, arXiv:2407.18057, 2024. -#. H\. Kikumoto, Y. Wang, B. Zhang & H. Jia. `Enhanced Wind Velocity and Pressure Measurement Around Buildings Using Physics-Informed Neural Networks: A Case Study with a Two-Dimensional Urban Street Canyon `_. *International Association of Building Physics*, pp. 390-396, 2024. #. Y\. Chen, H. Yu, C. Liu, J. Xie, J. Han & H. Dai. `Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system `_. *Applied Energy*, 373, 123874, 2024. -#. A\. Noorizadegan, R. Cavoretto, D. L. Young & C. S. Chen. `Stable weight updating: A key to reliable PDE solutions using deep learning `_. *Engineering Analysis with Boundary Elements*, 168, 105933, 2024. #. D\. Nguyen. `Advanced modeling of the childbirth system using different deep learning methods: from fetal skeleton segmentation to real-time soft tissue deformation `_. PhD thesis, Centrale Lille Institut, 2024. -#. A\. Jiao, Q. Yan, J. Harlim & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint*, arXiv:2407.05477, 2024. #. M\. Y. Hosseini & Y. Shiri. `Flow field reconstruction from sparse sensor measurements with physics-informed neural networks `_. *Physics of Fluids*, 36(7), 2024. -#. N\. Alzhanov, E. Y. K. Ng & Y. Zhao. `Three-Dimensional Physics-Informed Neural Network Simulation in Coronary Artery Trees `_. *Fluids*, 9(7), 153, 2024. -#. S\. Sripada, A. U. Gaitonde, J. A. Weibel & A. M. Marconnet. `Robust inverse parameter fitting of thermal properties from the laser-based Ångstrom method in the presence of measurement noise using physics-informed neural networks (PINNs) `_. *Journal of Applied Physics*, 135(22), 2024. -#. T\. Zou, T. Yajima & Y. Kawajiri. `A parameter estimation method for chromatographic separation process based on physics-informed neural network `_. *Journal of Chromatography A*, 465077, 2024. -#. H\. Wang, G. Fang, B. Gao, B. Wang & S. Meng. `Inversion of Spatially Distributed Elastic Moduli of 2.5D Woven Composites Based on DIC Strain Field Using PINN Method `_. *SSRN preprint*, 4851306, 2024. #. H\. Lu, Q. Wang, W. Tang & H. Liu. `Physics-informed neural networks for fully non-linear free surface wave propagation `_. *Physics of Fluids*, 36(6), 2024. -#. N\. Jha & E. Mallik. `GPINN with Neural Tangent Kernel Technique for Nonlinear Two Point Boundary Value Problems `_. *Neural Processing Letters*, 56(3), 192, 2024. #. Y\. Gao, P. Xiao & Z. Li. `Physics-Informed Neural Networks for Solving Underwater Two-dimensional Sound Field `_. *2024 OES China Ocean Acoustics (COA)*, pp. 1-4, IEEE, 2024. -#. N\. Jha & E. Mallik. `Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs `_. *Physica Scripta*, 2024. -#. R\. Fang, K. Zhang, K. Song, Y. Kai, Y. Li & B. Zheng. `A deep learning method for solving thermoelastic coupling problem `_. *Zeitschrift für Naturforschung A*, 2024. -#. J\. H. Harmening, F. Pioch, L. Fuhrig, F.-J. Peitzmann, D. Schramm & O. el Moctar. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, pp. 1-19, 2024. #. T\. Sahin, D. Wolff, M. von Danwitz & A. Popp. `Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam `_. *arXiv preprint*, arXiv:2405.08406, 2024. #. S\. K. Vemuri, T. Büchner, & J. Denzler. `Estimating soil hydraulic parameters for unsaturated flow using physics-informed neural networks `_. In *International Conference on Computational Science*, 338-351, Cham: Springer Nature Switzerland, 2024, June. #. N\. A. Niewiadomska, P. Maczuga, A. Oliver-Serra, L. Siwik, P. Sepulveda-Salaz, A. Paszyńska, M. Paszyński, & K. Pingali. `Modeling tsunami waves at the coastline of Valparaiso area of Chile with physics informed neural networks `_. In *International Conference on Computational Science*, 204-218, Cham: Springer Nature Switzerland, 2024, June. #. N\. Alzhanov, E. Y. K. Ng, & Y. Zhao. `Three-dimensional physics-informed neural network simulation in coronary artery trees `_. *Fluids*, 9(7), 2024. -#. S\. Sripada, A. U. Gaitonde, J. A. Weibel, & A. M. Marconnet. `Robust inverse parameter fitting of thermal properties from the laser-based Ångstrom method in the presence of measurement noise using physics-informed neural networks (PINNs) `_. *Journal of Applied Physics*, 135(22):225106, June 2024. +#. S\. Sripada, A. U. Gaitonde, J. A. Weibel, & A. M. Marconnet. `Robust inverse parameter fitting of thermal properties from the laser-based Ångstrom method in the presence of measurement noise using physics-informed neural networks (PINNs) `_. *Journal of Applied Physics*, 135(22):225106, June 2024. #. T\. Zou, T. Yajima, & Y. Kawajiri. `A parameter estimation method for chromatographic separation process based on physics-informed neural network `_. *Journal of Chromatography A*, 1730:465077, 2024. -#. H\. Wang, G. Fang, B. Gao, B. Wang, & S. Meng. `Inversion of spatially distributed elastic moduli of 2.5d woven composites based on dic strain field using PINN method `_, 2024. -#. N\. Jha & E. Mallik. `GPINN with neural tangent kernel technique for nonlinear two point boundary value problems `_. *Neural Processing Letters*, 56(3):192, May 2024. -#. H\. Zhang, L. Jiang, X. Chu, Y. Wen, L. Li, Y. Xiao, & L. Wang. `Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs `_, 2024. -#. N\. Jha & E. Mallik. `Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs `_. *Physica Scripta*, 99(7):076005, June 2024. -#. H\. Wu, H. Luo, Y. Ma, J. Wang, & M. Long. `RoPINN: Region optimized physics-informed neural networks `_, 2024. -#. J\. H. Harmening, F. Pioch, L. Fuhrig, F.-J. Peitzmann, D. Schramm, & O. el Moctar. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, May 2024. -#. T\. Sahin, D. Wolff, M. von Danwitz, & A. Popp. `Towards a hybrid digital twin: Physics-informed neural networks as surrogate model of a reinforced concrete beam `_, 2024. +#. N\. Jha & E. Mallik. `GPINN with neural tangent kernel technique for nonlinear two point boundary value problems `_. *Neural Processing Letters*, 56(3):192, May 2024. +#. H\. Zhang, L. Jiang, X. Chu, Y. Wen, L. Li, Y. Xiao, & L. Wang. `Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs `_. *Computer Physics Communications*, 308, p.109462. 2024. +#. N\. Jha & E. Mallik. `Gradient-based adaptive neural network technique for two-dimensional local fractional elliptic PDEs `_. *Physica Scripta*, 99(7):076005, June 2024. +#. J\. H. Harmening, F. Pioch, L. Fuhrig, F.-J. Peitzmann, D. Schramm, & O. el Moctar. `Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack `_. *Neural Computing and Applications*, May 2024. #. H\. Nganguia & D. Palaniappan. `Ciliary propulsion through non-uniform flows `_. *Journal of Fluid Mechanics*, 986:A14, 2024. #. A\. T. Deresse & T. T. Dufera. `Exploring physics-informed neural networks for the generalized nonlinear Sine-Gordon equation `_. *Applied Computational Intelligence and Soft Computing*, 2024(1):3328977, 2024. #. H\. Qiumei, M. Jiaxuan, & X. Zhen. `Mass-preserving spatio-temporal adaptive PINN for Cahn-Hilliard equations with strong nonlinearity and singularity `_, 2024. -#. Z.\ Zhang, J.-H. Lee, L. Sun, & G. X. Gu. `Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials `_. *PNAS Nexus*, 3(5):pgae186, May 2024. +#. Z.\ Zhang, J.-H. Lee, L. Sun, & G. X. Gu. `Weak-formulated physics-informed modeling and optimization for heterogeneous digital materials `_. *PNAS Nexus*, 3(5):pgae186, May 2024. #. S\. Gao, Q. Li, M. A. Gosalvez, X. Lin, Y. Xing, & Z. Zhou. `Helium focused ion beam damage in silicon: Physics-informed neural network modeling of Helium bubble nucleation and early growth `_, 2024. #. J\. Son, N. Park, H. Kwak, & J. Nam. `Optimizing a physics-informed machine learning model for pulsatile shear-thinning channel flow `_. *Journal of the Japanese Society of Rheology*, 52(2):113–122, 2024. -#. Raeisi, E., Yavuz, M., Khosravifarsani, M., & Fadaei, Y. `Mathematical modeling of interactions between colon cancer and immune system with a deep learning algorithm `_. *Eur. Phys. J. Plus*, 139(4):345, 2024. -#. K\. Yuan, C. Bauinger, X. Zhang, P. Baehr, M. Kirchhart, D. Dabert, A. Tousnakhoff, P. Boudier, & M. Paulitsch. `Fully-fused multi-layer perceptrons on Intel data center GPUs `_, 2024. -#. L\. Shang, Y. Zhao, S. Zheng, J. Wang, T. Zhang, & J. Wang. `Quantification of gradient energy coefficients using physics-informed neural networks `_. *International Journal of Mechanical Sciences*, 273:109210, 2024. +#. E\. Raeisi, M. Yavuz, M. Khosravifarsani, Y. Fadaei. `Mathematical modeling of interactions between colon cancer and immune system with a deep learning algorithm `_. *Eur. Phys. J. Plus*, 139(4), 345, 2024. #. Z\. Zhang, C. Lin, & B. Wang. `Physics-informed shape optimization using coordinate projection `_. *Scientific Reports*, 14, 6537, 2024. #. S\. Schoder & F. Kraxberger. `Feasibility study on solving the Helmholtz equation in 3D with PINNs `_. *arXiv preprint arXiv:2403.06623*, 2024. #. V\. Trávníková, D. Wolff, N. Dirkes, S. Elgeti, E. von Lieres, & M. Behr. `A model hierarchy for predicting the flow in stirred tanks with physics-informed neural networks `_. *arXiv preprint arXiv:2403.04576*, 2024. @@ -476,11 +411,11 @@ PINN #. T\. Zhang, R. Yan, S. Zhang, D. Yang, & A. Chen. `Application of Fourier feature physics-information neural network in model of pipeline conveying fluid `_. *Thin-Walled Structures*, 198, 111693, 2024. #. S\. Alkhadhr. `Modeling a clinical acoustic information system using physics-informed machine learning `_. 2024. #. J\. Shi, K. Manjunatha, M. Behr, F. Vogt, & S. Reese. `A physics-informed deep learning framework for modeling of coronary in-stent restenosis `_. *Biomechanics and Modeling in Mechanobiology*, 23, 615-629, 2024. -#. C\. Kou, Y. Yin, Y. Zeng, S. Jia, Y. Luo, & X. Yuan. `Physics-informed neural network integrate with unclosed mechanism model for turbulent mass transfer `_. *Chemical Engineering Science*, 288, 119752, 2024. -#. B\. Jang, A. A. Kaptanoglu, R. Gaur, S. Pan, M. Landreman, & W. Dorland. `Grad–Shafranov equilibria via data-free physics informed neural networks `_. *Physics of Plasmas*, 31, 3, 2024. +#. C\. Kou, Y. Yin, Y. Zeng, S. Jia, Y. Luo, & X. Yuan. `Physics-informed neural network integrate with unclosed mechanism model for turbulent mass transfer `_. *Chemical Engineering Science*, 288, 119752, 2024. +#. B\. Jang, A. A. Kaptanoglu, R. Gaur, S. Pan, M. Landreman, & W. Dorland. `Grad–Shafranov equilibria via data-free physics informed neural networks `_. *Physics of Plasmas*, 31, 3, 2024. #. Z\. Wang, R. Keller, X. Deng, K. Hoshino, T. Tanaka, & Y. Nakahira. `Physics-informed representation and learning: Control and risk quantification `_. In *Proceedings of the AAAI Conference on Artificial Intelligence*, 38, 19, 21699-21707, 2024, March. #. M\. Mircea, D. Garlaschelli, & S. Semrau. `Inference of dynamical gene regulatory networks from single-cell data with physics informed neural networks `_. *arXiv preprint arXiv:2401.07379*, 2024. -#. R\. Casado-Vara, M. Severt, A. Díaz-Longueira, Á.M.D. Rey, & J.L. Calvo-Rolle. `Dynamic malware mitigation strategies for IoT networks: A mathematical epidemiology approach `_. *Mathematics*, 12, 250, 2024. +#. R\. Casado-Vara, M. Severt, A. Díaz-Longueira, Á. M. Rey, & J. L. Calvo-Rolle. `Dynamic Malware Mitigation Strategies for IoT Networks: A Mathematical Epidemiology Approach `_. *Mathematics*, 12(2), 250, 2024. #. P\. Karnakov, S. Litvinov, & P. Koumoutsakos. `Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks `_. *PNAS Nexus*, 3, pgae005, 2024. #. J\. Seo. `Solving real-world optimization tasks using physics-informed neural computing `_. *Scientific Reports*, 14(1), 202, 2024. #. J\. Wu, Y. Wu, G. Zhang, & Y. Zhang. `Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems `_. *Journal of Computational Physics*, 112761, 2024. @@ -495,7 +430,6 @@ PINN #. P\. Brendel, V. Medvedev, & A. Rosskopf. `Physics-informed neural networks for magnetostatic problems on axisymmetric transformer geometries `_. *IEEE Journal of Emerging and Selected Topics in Industrial Electronics*, 2023. #. T\. Zhang, D. Wang, & Y. Lu. `RheologyNet: A physics-informed neural network solution to evaluate the thixotropic properties of cementitious materials `_. *Cement and Concrete Research*, 168, 107157, 2023. #. S\. C. Salas, A. O. Alvarado, F. Ortega-culaciati, & P. escapil-inchauspé. `Physics informed neural network for quasistatic fault slip forward and inverse problems `_. 2023. -#. Z\. Wang, R. Keller, X. Deng, K. Hoshino, T. Tanaka, & Y. Nakahira. `Physics-informed representation and learning: Control and risk quantification `_. *arXiv preprint arXiv:2312.10594*, 2023. #. C\. Li, & Z. Han. `Shallow water equations-fused dam-break wave propagation prediction model ensembled with a training process resampling method `_. *2023 International Conference on Intelligent Computing and Next Generation Networks（ICNGN)*, 1-6. 10.1109/ICNGN59831.2023.10396666. #. X\. Yang, Y. Du, L. Li, Z. Zhou, & X. Zhang. `Physics-informed neural network for model prediction and dynamics parameter identification of collaborative robot joints `_. *IEEE Robotics and Automation Letters*, vol. 8, no. 12, pp. 8462-8469, 2023. #. S\. H. Radbakhsh, K. Zandi, & M. Nik-bakht. `Physics-informed neural network for analyzing elastic beam behavior `_. *Structural Health Monitoring*, 2023. @@ -504,11 +438,10 @@ PINN #. J\. Shi, K. Manjunatha, & S. Reese. `Deep learning-based surrogate modeling of coronary in-stent restenosis `_. *Proceedings in Applied Mathematics and Mechanics*, 23, e202300090. #. Y\. Jiang, W. Yang, Y. Zhu, & L. Hong. `Entropy structure informed learning for solving inverse problems of differential equations `_. *Chaos, Solitons & Fractals*, Volume 175, Part 2, 2023. #. A\. Ogueda-Oliva, & P. Seshaiyer. `Literate programming for motivating and teaching neural network-based approaches to solve differential equations `_. *International Journal of Mathematical Education in Science and Technology*, 55(2), 509–542. -#. B\. Jang, A. A. Kaptanoglu, R. Gaur, S. Pan, M. Landreman, & W. Dorland. `Grad-Shafranov equilibria via data-free physics informed neural networks `_. *arXiv preprint arXiv:2311.13491*, 2023. #. C\. Li. `Enhancing Navier-Stokes flow learning through the level set approach `_. *Available at SSRN 4641595*. #. X\. Zhu, X. Hu, & P. Sun. `Physics-informed neural networks for solving dynamic two-phase interface problems `_. *SIAM Journal on Scientific Computing*, 45(6), A2912-A2944, 2023. #. H\. Patel, A. Panda, T. Nikolaienko, S. Jaso, A. Lopez, & K. Kalyanaraman. `Accurate and fast Fischer-Tropsch reaction microkinetics using PINNs `_. *arXiv preprint arXiv:2311.10456*, 2023. -#. J\. Plata Salas. `Física asistida por redes neuronales artificiales `_. *Repositorio Nacional CONACYT*, 2023. +#. J\. Plata Salas. `FísicaQuantification of gradient energy coefficients using physics-informed neural networksiciales `_. *Repositorio Nacional CONACYT*, 2023. #. N\. Namaki, M. R. Eslahchi, & R. Salehi. `The use of physics-informed neural network approach to image restoration via nonlinear PDE tools `_. *Computers & Mathematics with Applications*, 152, 355-363, 2023. #. A\. Hvatov, D. Aminev, & N. Demyanchuk. `Easy to learn hard to master - how to solve an arbitrary equation with PINN `_. *NeurIPS 2023 AI for Science Workshop*, 2023. #. H\. Son, H. Cho, & H. J. Hwang. `Physics-informed neural networks for microprocessor thermal management model `_. *IEEE Access*, 11, 122974-122979, 2023. @@ -530,11 +463,8 @@ PINN #. Y\. Xu, & T. Zeng. `Multi-grade deep learning for partial differential equations with applications to the Burgers equation `_. *arXiv preprint arXiv:2309.07401*, 2023. #. G\. Cappellini, G. Trappolini, E. Staffetti, A. Cristofaro, & M. Vendittelli. `Adaptive estimation of the Pennes' bio-heat equation-II: A NN-based implementation for real-time applications `_. #. M\. Vais. `Deep learning for the solution of differential equations `_. -#. L\. Novák, H. Sharma, & M. D. Shields. `Physics-informed polynomial chaos expansions `_. *arXiv preprint arXiv:2309.01697*, 2023. #. C\. Coelho, M. F. P. Costa, & L. L. Ferrás. `The influence of the optimization algorithm in the solution of the fractional Laplacian equation by neural networks `_. *In AIP Conference Proceedings (Vol. 2849, No. 1). AIP Publishing*, 2023. -#. S\. Song, & H. Jin. `Identifying constitutive parameters for complex hyperelastic solids using physics-informed neural networks `_. *arXiv preprint arXiv:2308.15640*, 2023. #. A\. Moreira, M. Philipps, & N. Van Riel. `Parameter estimation of a physiological diabetes model using neural networks `_. *In 2023 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-8). IEEE*, 2023. -#. T\. Sahin, M. von Danwitz, & M. Popp. `Solving forward and inverse problems of contact mechanics using physics-informed neural networks `_. *arXiv preprint arXiv:2308.12716*, 2023. #. A\. G. Ogueda-Oliva, A. G. Martínez-Salinas, V. Arunachalam, & P. Seshaiyer. `Machine learning for predicting the dynamics of infectious diseases during travel through physics-informed neural networks `_. *Journal of Machine Learning for Modeling and Computing*, 4(3), 2023. #. S\. Y. Xu, Q. Zhou, & W. Liu. `Prediction of soliton evolution and equation parameters for NLS-MB equation based on the phPINN algorithm `_. *Nonlinear Dynamics*, 111(19), 18401-18417, 2023. #. T\. Kapoor, A. Chandra, D. M. Tartakovsky, H. Wang, A. Nunez, & R. Dollevoet. `Neural oscillators for generalization of physics-informed machine learning `_. *arXiv preprint arXiv:2308.08989*, 2023. @@ -545,8 +475,6 @@ PINN #. H\. W. Park, & J. H. Hwang. `Predicting the early-age time-dependent behaviors of a prestressed concrete beam by using physics-informed neural network `_. *Sensors*, 23(14), 6649, 2023. #. D\. Bonnet-Eymard, A. Persoons, M. G. Faes, & D. Moens. `Quantifying uncertainty of physics-informed neural networks for continuum mechanics applications `_. #. M\. Z. Asadzadeh, K. Roppert, & P. Raninger. `Material data identification in an induction hardening test rig with physics-informed neural networks `_. *Materials*, 16(14), 5013, 2023. -#. A\. Ogueda, E. Martinez, V. Arunachalam, & P. Seshaiyer. `Machine learning for predicting the dynamics of infectious diseases during travel through physics informed neural networks `_. *Journal of Machine Learning for Modeling and Computing*, 2023. -#. A\. Serebrennikova, R. Teubler, L. Hoffellner, E. Leitner, U. Hirn, & K. Zojer. `Physics informed neural networks reveal valid models for reactive diffusion of volatiles through paper `_. *Chemical Engineering Science*, 119636, 2023. #. W\. Xuan, H. Lou, S. Fu, Z. Zhang, & N. Ding. `Physics-informed deep learning method for the refrigerant filling mass flow metering `_. *Flow Measurement and Instrumentation*, 93, 102418, 2023. #. S\. Alkhadhr and M. Almekkawy. `Wave equation modeling via physics-informed neural networks: Models of soft and hard constraints for initial and boundary conditions `_. *Sensors*, 23(5), 2023. #. M\. Bazmara, M. Mianroodi, and M. Silani. `Application of physics-informed neural networks for nonlinear buckling analysis of beams `_. *Acta Mechanica Sinica*, 39(6):422438, 2023. @@ -558,7 +486,6 @@ PINN #. Z\. Gong, Y. Chu, and S. Yang. `Physics-informed neural networks for solving 2-D magnetostatic fields `_. *IEEE Transactions on Magnetics*, 59(11):1-5, 2023. #. M\. A. Haddou. `Quasi-normal modes of near-extremal black holes in dRGT massive gravity using physics-informed neural networks (PINNs) `_. 2023. #. Z\. Hao, J. Yao, C. Su, H. Su, Z. Wang, F. Lu, Z. Xia, Y. Zhang, S. Liu, L. Lu, & J. Zhu. `PINNacle: A comprehensive benchmark of physics-informed neural networks for solving PDEs `_. *arXiv preprint arXiv:2306.08827*, 2023. -#. J\. H. Harmening, F. Pioch, L. Fuhrig, F.-J. Peitzmann, D. Schramm, and el Moctar. `Data-assisted training of a physics-informed neural network to predict the Reynolds-averaged turbulent flow field around a stalled airfoil under variable angles of attack `_. *Preprints*, 2023. #. H\. Huang, Y. Li, Y. Xue, K. Zhang, and F. Yang. `A deep learning approach for solving diffusion-induced stress in large-deformed thin film electrodes `_. *Journal of Energy Storage*, 63:107037, 2023. #. Y\. Huang, Z. Xu, C. Qian, & L. Liu. `Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) `_. *Journal of Computational Physics*, p.112003, 2023. #. H\. Jung, J. Gupta, B. Jayaprakash, M. Eagon, H. P. Selvam,C. Molnar, W. Northrop, and S. Shekhar. `A survey on solving and discovering differential equations using deep neural networks `_. 2023. @@ -642,7 +569,6 @@ PINN #. S\. Markidis. `The old and the new: Can physics-informed deep-learning replace traditional linear solvers? `_. *Frontiers in Big Data*, 4:669097, 2021. #. S\. Alkhadhr, X. Liu, & M. Almekkawy. `Modeling of the forward wave propagation using physics-informed neural networks `_. *2021 IEEE International Ultrasonics Symposium (IUS)*, pp. 1--4, 2021. #. L\. Lu, R. Pestourie, W. Yao, Z. Wang, F. Verdugo, & S. Johnson. `Physics-informed neural networks with hard constraints for inverse design `_. *SIAM Journal on Scientific Computing*, 43(6), B1105--B1132, 2021. -#. Z\. Li, H. Zheng, N. Kovachki, D. Jin, H. Chen, B. Liu, K. Azizzadenesheli, & A. Anandkumar. `Physics-informed neural operator for learning partial differential equations `_. *arXiv preprint arXiv:2111.03794*, 2021. #. K\. Goswami, A. Sharma, M. Pruthi, & R. Gupta. `Study of drug assimilation in human system using physics informed neural networks `_. *arXiv preprint arXiv:2110.05531*, 2021. #. C\. Hennigan. `The primal Hamiltonian: A new global approach to monetary policy `_. 2021. #. S\. Lee, & T. Kadeethum. `Physics-informed neural networks for solving coupled flow and transport system `_. 2021. @@ -666,33 +592,25 @@ PINN Deep neural operators -------- -#. J\. He, D. Pal, A. Najafi, et al. `Material-Response-Informed DeepONet and Its Application to Polycrystal Stress–Strain Prediction in Crystal Plasticity `_. *JOM*, 76, 5744–5754, 2024. #. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477*, 2024. #. J\. Park, & N. Kang. `Point-DeepONet: A Deep Operator Network Integrating PointNet for Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions `_. *arXiv preprint arXiv:2412.18362*, 2024. #. K\. Lv, J. Wang, Y. Zhang, & H. Yu. `Neural Operators for Adaptive Control of Freeway Traffic `_. *arXiv preprint arXiv:2410.20708*, 2024. #. Z\. Li, H. Zheng, N. Kovachki, D. Jin, H. Chen, B. Liu, K. Azizzadenesheli, & A. Anandkumar. `Physics-Informed Neural Operator for Learning Partial Differential Equations `_. *Association for Computing Machinery*, 1(3), September 2024. #. C\. García-Cervera, M. Kessler, P. Pedregal, & F. Periago. `Universal approximation of set-valued maps and DeepONet approximation of the controllability map `_. *ResearchGate*, December 2024. #. J\. He, S. Koric, D. Abueidda, A. Najafi, & I. Jasiuk. `Geom-DeepONet: A point-cloud-based deep operator network for field predictions on 3D parameterized geometries `_. *Computer Methods in Applied Mechanics and Engineering*, Volume 429, 117130, 2024. -#. M\. Lamarque, L. Bhan, R. Vazquez, & M. Krstic. `Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation `_. *arXiv preprint arXiv:2401.02511*, 2024. #. S\. Kushwaha, J. Park, S. Koric, J. He, I. Jasiuk, & D. Abueidda. `Advanced deep operator networks to predict multiphysics solution fields in materials processing and additive manufacturing `_. *Additive Manufacturing*, Volume 88, 104266, 2024. #. O\. Ovadia, A. Kahana, P. Stinis, E. Turkel, D. Givoli, & G. E. Karniadakis. `ViTO: Vision Transformer-Operator `_. *Computer Methods in Applied Mechanics and Engineering*, Volume 428, 117109, 2024. #. Z\. Jiang, M. Zhu, & L. Lu. `Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration `_. *Reliability Engineering & System Safety*, Volume 251, 110392, 2024. #. Q\. Meng, Y. Li, Z. Deng, X. Liu, G. Chen, Q. Wu, C. Liu, & X. Hao. `A general reduced-order neural operator for spatio-temporal predictive learning on complex spatial domains `_. *arXiv preprint arXiv:2409.05508*, 2024. -#. A\. A. Aghaei, M. M. Moghaddam, & K. Parand. `PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems `_. *arXiv preprint arXiv:2409.01899*, 2024. #. K\. Lv, J. Wang, & Y. Cao. `Neural Operator Approximations for Boundary Stabilization of Cascaded Parabolic PDEs `_. *International Journal of Adaptive Control and Signal Processing*, Wiley Online Library, 2024. #. B\. Ahmed, Y. Qiu, D. W. Abueidda, W. El-Sekelly, B. G. de Soto, T. Abdoun, & M. E. Mobasher. `Physics-informed DeepONet with stiffness-based loss functions for structural response prediction `_. *arXiv preprint arXiv:2409.00994*, 2024. #. P\. Gao, G. E. Karniadakis, & P. Stinis. `Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks `_. *arXiv preprint arXiv:2408.03263*, 2024. -#. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477*, 2024. #. L\. Xiao, G. Mei, & N. Xu. `Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region `_. *Journal of Rock Mechanics and Geotechnical Engineering*, Elsevier, 2024. -#. A\. Jiao, Q. Yan, J. Harlim, & L. Lu. `Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators `_. *arXiv preprint arXiv:2407.05477v1*, 2024. #. G\. Fabiani, I. G. Kevrekidis, C. Siettos, & A. N. Yannacopoulos. `RandONet: Shallow-networks with random projections for learning linear and nonlinear operators `_. *Computer Methods in Applied Mechanics and Engineering*, 429:117130, 2024. #. A\. Jiao, H. He, R. Ranade, J. Pathak, & L. Lu. `One-shot learning for solution operators of partial differential equations `_. *arXiv preprint arXiv:2104.05512*, 2024. -#. L\. Xiao, G. Mei, & N. Xu. `Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region `_. *Journal of Rock Mechanics and Geotechnical Engineering*, 2024. -#. J\. He, S. Koric, D. Abueidda, A. Najafi, & I. Jasiuk. `Geom-DeepONet: A point-cloud-based deep operator network for field predictions on 3D parameterized geometries `_. *Computer Methods in Applied Mechanics and Engineering*, 429:117130, 2024. -#. S\. Kushwaha, J. Park, S. Koric, J. He, I. Jasiuk, & D. Abueidda. `Advanced deep operator networks to predict multiphysics solution fields in materials processing and additive manufacturing `_. *Additive Manufacturing*, 88:104266, 2024. #. S\. Zampini, U. Zerbinati, G. Turkyyiah, & D. Keyes. `PETScML: Second-order solvers for training regression problems in Scientific Machine Learning `_. In *Proceedings of the Platform for Advanced Scientific Computing Conference*, 1-12, 2024, June. #. L\. Branca & A. Pallottini. `Emulating the interstellar medium chemistry with neural operators `_. *Astronomy & Astrophysics*, 684, A203, 2024. -#. J\. Hayford, J. Goldman-Wetzler, E. Wang, & L. Lu. `Speeding up and reducing memory usage for scientific machine learning via mixed precision `_. *Computer Methods in Applied Mechanics and Engineering*, 428, 117093, 2024. +#. J\. Hayford, J. Goldman-Wetzler, E. Wang, & L. Lu. `Speeding up and reducing memory usage for scientific machine learning via mixed precision `_. *Computer Methods in Applied Mechanics and Engineering*, 428, 117093, 2024. #. K\. Kobayashi, J. Daniell, & S.B. Alam. `Improved generalization with deep neural operators for engineering systems: Path towards digital twin `_. *Engineering Applications of Artificial Intelligence*, 131, 107844, 2024. #. K\. Kobayashi & S.B. Alam. `Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems `_. *Scientific Reports*, 14, 2101, 2024. #. H\. Liu, B. Dahal, R. Lai, & W. Liao. `Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning `_. *arXiv preprint arXiv:2401.10490*, 2024. @@ -703,16 +621,13 @@ Deep neural operators #. A\. Xavier. `Solving Heat Conduction Problems with DeepONets `_. 2023. #. L\. Xu, H. Zhang, & M. Zhang. `Training a deep operator network as a surrogate solver for two-dimensional parabolic-equation models `_. *The Journal of the Acoustical Society of America*, 154(5), 3276-3284, 2023. #. N\. Ford, V. J. Leon, H. Merman, J. Gilbert, & A. New. `Data-efficient operator learning for solving high Mach number fluid flow problems `_. *arXiv preprint arXiv:2311.16860*, 2023. -#. J\. He, S. Kushwaha, J. Park, S. Koric, D. Abueidda, & I. Jasiuk. `Multi-component predictions of transient solution fields with sequential deep operator network `_. *arXiv preprint arXiv:2311.11500*, 2023. #. B\. Chen, C. Wang, W. Li, & H. Fu. `A hybrid Decoder-DeepONet operator regression framework for unaligned observation data `_. *arXiv preprint arXiv:2308.09274*, 2023. #. K\. Kobayashi, & S. B. Alam. `Potential of deep operator networks in digital twin-enabling technology for nuclear system `_. *arXiv preprint arXiv:2308.07523*, 2023. #. J\. He, S. Kushwaha, J. Park, S. Koric, D. Abueidda, & I. Jasiuk. `Sequential deep operator networks (S-DeepONet) for predicting full-field solutions under time-dependent loads `_. *Engineering Applications of Artificial Intelligence*, 127:107258, 2024. #. E\. L. Bolager, I. Burak, C. Datar, Q. Sun, & F. Dietrich. `Sampling weights of deep neural networks `_. 2023. #. V\. Fanaskov, T. Yu, A. Rudikov, & I. Oseledets. `General covariance data augmentation for neural PDE solvers `_. 2023. #. J\. He, S. Koric, S. Kushwaha, J. Park, D. Abueidda, & I. Jasiuk. `Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads `_. *Computer Methods in Applied Mechanics and Engineering*, 415:116277, 2023. -#. Z\. Jiang, M. Zhu, D. Li, Q. Li, Y. Yuan, & L. Lu. `Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration `_. *arXiv preprint arXiv:2303.04778*, 2023. #. K\. Kobayashi, J. Daniell, & S. B. Alam. `Operator learning framework for digital twin and complex engineering systems `_. 2023. -#. O\. Ovadia, A. Kahana, P. Stinis, E. Turkel, & G. E. Karniadakis. `ViTO: Vision transformer-operator `_. 2023. #. M\. Zhu, S. Feng, Y. Lin, & L. Lu. `Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness `_. *Computer Methods in Applied Mechanics and Engineering*, 416, 116300, 2023. #. S\. Mao, R. Dong, L. Lu, K. M. Yi, S. Wang, & P. Perdikaris. `PPDONet: Deep operator networks for fast prediction of steady-state solutions in disk-planet systems `_. *The Astrophysical Journal Letters*, 950(2), L12, 2023. #. S\. Wang, & P. Perdikaris. `Long-time integration of parametric evolution equations with physics-informed deeponets `_. *Journal of Computational Physics*, 475, p.111855, 2023. From b5a57e086cd1fec2c75a397c5c134b39ac7a8bf7 Mon Sep 17 00:00:00 2001 From: KangyuWeng Date: Mon, 20 Jan 2025 21:10:03 -0500 Subject: [PATCH 3/4] add the comma --- docs/user/research.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user/research.rst b/docs/user/research.rst index 45e0cf5a1..e70d729ed 100644 --- a/docs/user/research.rst +++ b/docs/user/research.rst @@ -294,7 +294,7 @@ DeepXDE has been used in `Shell `_, `SoftServe `_, `Quantiph `_, - `Moldex3D `_ + `Moldex3D `_, Here is a list of research papers that used DeepXDE. If you would like your paper to appear here, open an issue in the GitHub "Issues" section. From 747ef5df00f261ff78773e991071453b0cd67be0 Mon Sep 17 00:00:00 2001 From: KangyuWeng Date: Mon, 20 Jan 2025 21:21:08 -0500 Subject: [PATCH 4/4] add two more commas --- docs/user/research.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/user/research.rst b/docs/user/research.rst index e70d729ed..d3706161b 100644 --- a/docs/user/research.rst +++ b/docs/user/research.rst @@ -195,7 +195,7 @@ DeepXDE has been used in `Xinjiang University `_, `LUT University `_, `University of Las Palmas de Gran Canaria `_, - `Nanjing University of Aeronautics and Astronautics ` + `Nanjing University of Aeronautics and Astronautics `_, `Lahore University of Management Sciences `_, `Hangzhou Dianzi University `_, `London South Bank University `_, @@ -269,7 +269,7 @@ DeepXDE has been used in `Mitsubishi Electric Research Laboratories `_, `Forschungszentrum Jülich `_, `China Ship Scientific Research Center `_, - `Yanqi Lake Beijing Institute of Mathematical Sciences and Applications `_ + `Yanqi Lake Beijing Institute of Mathematical Sciences and Applications `_, `Korea Institute of Fusion Energy `_, `Fraunhofer Heinrich Hertz Institute `_, `Northwest Institute of Nuclear Technology `_,