From fdd2cd32c1e760327c2514587eb63fa02fdea8fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A1n=20Drgo=C5=88a?= Date: Tue, 9 Jul 2024 06:13:36 -0700 Subject: [PATCH 1/4] Update README.md --- README.md | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index b65e845a..daa16c7c 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ Part 2: NeuroMANCER syntax tutorial: variables, constraints, and objectives. Part 3: NeuroMANCER syntax tutorial: modules, Node, and System class. -### Parametric Programming +### Learning to Optimize (L2O) for Parametric Programming + Open In Colab Part 1: Learning to solve a constrained optimization problem. @@ -87,8 +87,10 @@ Part 4: Learning to solve a constrained optimization problem with the projected + Open In Colab Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasible set. ++ Open In Colab +Part 6: Learning to optimize with metric learning for Operator Splitting layers. -### Ordinary Differential Equations (ODEs) +### System Identification of Ordinary Differential Equations (ODEs) + Open In Colab Part 1: Neural Ordinary Differential Equations (NODEs) @@ -125,7 +127,7 @@ Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasi + Open In Colab Part 5: Damped Pendulum (stacked PINN) + Open In Colab Part 6: Navier-Stokes equation (lid-driven cavity flow, steady-state, KAN) -### Control +### Learning to Control (L2C) with Differentiable Models + Open In Colab Part 1: Learning to stabilize a linear dynamical system. @@ -177,7 +179,7 @@ Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasi Open In Colab Part 4: Defining Custom Training Logic via Lightning Modularized Code. -### Stochastic Differential Equation Examples +### Stochastic Differential Equations (SDEs) + Open In Colab LatentSDEs: "System Identification" of Stochastic Processes using Neuromancer x TorchSDE @@ -372,6 +374,12 @@ See the [license](https://github.com/pnnl/neuromancer/blob/master/LICENSE.md) fo ## Publications ++ [Jan Drgona, Aaron Tuor, Draguna Vrabie, Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024](https://ieeexplore.ieee.org/abstract/document/10479163) ++ [Renukanandan Tumu, Wenceslao Shaw Cortez, Ján Drgoňa, Draguna L. Vrabie, Sonja Glavaski, Differentiable Predictive Control for Large-Scale Urban Road Networks, arXiv:2406.10433, 2024](https://arxiv.org/abs/2406.10433) ++ [Ethan King, James Kotary, Ferdinando Fioretto, Jan Drgona, Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming, arXiv:2404.00882, 2024](https://arxiv.org/abs/2404.00882) ++ [James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona, Neural Differential Algebraic Equations, arXiv:2403.12938, 2024](https://arxiv.org/abs/2403.12938) ++ [Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar, A Robust, Efficient Predictive Safety Filter, arXiv:2311.08496, 2024](https://arxiv.org/abs/2311.08496) ++ [Shrirang Abhyankar, Jan Drgona, Andrew August, Elliott Skomski, Aaron Tuor, Neuro-physical dynamic load modeling using differentiable parametric optimization, 2023 IEEE Power & Energy Society General Meeting (PESGM), 2023](https://ieeexplore.ieee.org/abstract/document/10253098) + [James Koch, Zhao Chen, Aaron Tuor, Jan Drgona, Draguna Vrabie, Structural Inference of Networked Dynamical Systems with Universal Differential Equations, arXiv:2207.04962, (2022)](https://aps.arxiv.org/abs/2207.04962) + [Ján Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Learning Stochastic Parametric Differentiable Predictive Control Policies, IFAC ROCOND conference (2022)](https://www.sciencedirect.com/science/article/pii/S2405896322015877) + [Sayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie, Neural Lyapunov Differentiable Predictive Control, IEEE Conference on Decision and Control Conference 2022](https://arxiv.org/abs/2205.10728) @@ -391,7 +399,7 @@ See the [license](https://github.com/pnnl/neuromancer/blob/master/LICENSE.md) fo ```yaml @article{Neuromancer2023, title={{NeuroMANCER: Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations}}, - author={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Vrabie, Draguna}, + author={Drgona, Jan and Tuor, Aaron and Koch, James and Shapiro, Madelyn and Jacob, Bruno and Vrabie, Draguna}, Url= {https://github.com/pnnl/neuromancer}, year={2023} } @@ -399,11 +407,10 @@ See the [license](https://github.com/pnnl/neuromancer/blob/master/LICENSE.md) fo ## Development team -**Lead developers**: [Jan Drgona](https://drgona.github.io/), [Aaron Tuor](https://sw.cs.wwu.edu/~tuora/aarontuor/) -**Active core developers**: Madelyn Shapiro, Bruno Jacob, Rahul Birmiwal +**Active core developers**: [Jan Drgona](https://drgona.github.io/), [Rahul Birmiwal](https://www.linkedin.com/in/rahul-birmiwal009/), [Bruno Jacob](https://brunopjacob.github.io/) +**Notable contributors**: [Aaron Tuor](https://sw.cs.wwu.edu/~tuora/aarontuor/), Madelyn Shapiro, James Koch, Seth Briney, Bo Tang, Ethan King, Shrirang Abhyankar, +Elliot Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard **Scientific advisors**: Draguna Vrabie, Panos Stinis -**Notable contributors**: James Koch, Seth Briney, Bo Tang, Ethan King, Shrirang Abhyankar, -Mia Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard Open-source contributions made by: From 0bc7dbe9cc22ae33c1081f972921533bd2cdbd70 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A1n=20Drgo=C5=88a?= Date: Tue, 9 Jul 2024 06:14:21 -0700 Subject: [PATCH 2/4] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index daa16c7c..535dc9ae 100644 --- a/README.md +++ b/README.md @@ -87,7 +87,7 @@ Part 4: Learning to solve a constrained optimization problem with the projected + Open In Colab Part 5: Using Cvxpylayers for differentiable projection onto the polytopic feasible set. -+ Open In Colab ++ Open In Colab Part 6: Learning to optimize with metric learning for Operator Splitting layers. ### System Identification of Ordinary Differential Equations (ODEs) From c243de8c971bf9555add6bc5eca13fa3d3c42944 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A1n=20Drgo=C5=88a?= Date: Tue, 9 Jul 2024 06:16:43 -0700 Subject: [PATCH 3/4] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 535dc9ae..339a98fc 100644 --- a/README.md +++ b/README.md @@ -407,9 +407,8 @@ See the [license](https://github.com/pnnl/neuromancer/blob/master/LICENSE.md) fo ## Development team -**Active core developers**: [Jan Drgona](https://drgona.github.io/), [Rahul Birmiwal](https://www.linkedin.com/in/rahul-birmiwal009/), [Bruno Jacob](https://brunopjacob.github.io/) -**Notable contributors**: [Aaron Tuor](https://sw.cs.wwu.edu/~tuora/aarontuor/), Madelyn Shapiro, James Koch, Seth Briney, Bo Tang, Ethan King, Shrirang Abhyankar, -Elliot Skomski, Stefan Dernbach, Zhao Chen, Christian Møldrup Legaard +**Active core developers**: [Jan Drgona](https://drgona.github.io/), [Rahul Birmiwal](https://www.linkedin.com/in/rahul-birmiwal009/), [Bruno Jacob](https://brunopjacob.github.io/) +**Notable contributors**: [Aaron Tuor](https://sw.cs.wwu.edu/~tuora/aarontuor/), Madelyn Shapiro, James Koch, Seth Briney, Bo Tang, Ethan King, Elliot Skomski, Zhao Chen, Christian Møldrup Legaard **Scientific advisors**: Draguna Vrabie, Panos Stinis Open-source contributions made by: From 77a606bf3fd57f6ba3b4c6e12cbe7223ab62baad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A1n=20Drgo=C5=88a?= Date: Wed, 10 Jul 2024 10:12:13 -0700 Subject: [PATCH 4/4] Update RELEASE_NOTES.md --- RELEASE_NOTES.md | 1 + 1 file changed, 1 insertion(+) diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 9e4939d0..9e2c14e7 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -7,6 +7,7 @@ + New feature: TorchSDE integration with Neuromancer core library, namely `torchsde.sdeint()`. Motivating example for system ID on stochastic process found in examples/sdes/sde_walkthrough.ipynb + New feature: Stacked physics-informed neural networks + New feature: SINDy -- sparse system identification of nonlinear dynamical systems ++ New feature: differentiable proximal operators in operator splitting methods for learning to optimize ### Version 1.5.0 Release Notes + New Feature: PyTorch Lightning Integration with NeuroMANCER core library. All these features are opt-in.