Stars
Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation
Finite element modeling of glaciers and ice sheets
tmm_fast is a lightweight package to speed up optical planar multilayer thin-film device computation. Developed by Alexander Luce (@Nerrror) in cooperation with Heribert Wankerl (@HarryTheBird).
DSL and compiler framework for automated finite-differences and stencil computation
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
A Julia framework for invertible neural networks
(Conditional) Normalizing Flows in PyTorch. Offers a wide range of (conditional) invertible neural networks.
Code for paper "Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach" https://arxiv.org/abs/2206.02433
A normalizing flow using Bernstein polynomials for conditional density estimation.
Code for the research paper "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference".
Code for the paper "Analyzing inverse problems with invertible neural networks." (2018)
Code for the paper "Guided Image Generation with Conditional Invertible Neural Networks" (2019)
Automatic Differentiation Library for Computational and Mathematical Engineering
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
PyLlama enables to calculate the reflection and transmission spectra of an arbitrary multilayer stack whose layers are made of dispersive or non-dispersive, absorbing or non absorbing, isotropic or…