Binder for ngsolve

supplementary_scripts_for_JCP_manuscript

Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs

PECANNs: Physics and Equality Constrained Artificial Neural Networks

Investigating PINNs

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

PINN, DGM and DRM

QMC sampling for deep ritz method

Implementation of the Deep Ritz method and the Deep Galerkin method

PyTorch Implementation of Physics-informed Neural Networks

Next generation FEniCS Form Compiler for finite element forms

Finite Element Module for Julia that focusses on gradient-robust discretisations and multiphysics problems

HArD::Core2D (Hybrid Arbitrary Degree::Core 2D) - Library to implement schemes with edge and cell polynomial unknowns on 2D generic meshes

PDE-Net: Learning PDEs from Data

source code of learning to discretize solving 1d scalar conservation laws via deep reinforcement learning

Finite Element Analysis Library in Python

Isogeometric Analysis with hierarchical refinements

FEM code based on the library deal.II to solve 3D problems using unfitted meshes under the cut-cell approach.

multiphenics - easy prototyping of multiphysics problems in FEniCS

Root for HArDCore2D and HArDCore3D projects

Finite Element Multiphysics PARallel solvers. Official mirror from https://gitlab.com/fempar/fempar

Firedrake is an automated system for the portable solution of partial differential equations using the finite element method (FEM)

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Finite volume toolbox for Matlab/Octave