alialaradi / DeepGalerkinMethodLinks
Companion code for "Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning" by A. Al-Aradi, A. Correia, D. Naiff, G. Jardim and Y. Saporito (https://arxiv.org/abs/1811.08782)
☆122Updated 6 years ago
Alternatives and similar repositories for DeepGalerkinMethod
Users that are interested in DeepGalerkinMethod are comparing it to the libraries listed below
Sorting:
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆156Updated 5 years ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆178Updated 4 years ago
- Implementation of the Deep Ritz method and the Deep Galerkin method☆60Updated 5 years ago
- ☆42Updated 5 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆78Updated 3 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 years ago
- ☆264Updated 2 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆147Updated 5 years ago
- ☆226Updated 4 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- ☆63Updated 6 years ago
- hPINN: Physics-informed neural networks with hard constraints☆147Updated 3 years ago
- Tutorials on deep learning, Python, and dissipative particle dynamics☆199Updated 3 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆217Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Different methods of solving partial differential equations with neural networks☆17Updated 4 years ago
- ☆99Updated 4 years ago
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆280Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆84Updated 2 months ago
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆22Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆75Updated last week
- Deep BSDE solver in TensorFlow☆284Updated 6 months ago
- Solving PDEs with NNs☆55Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- ☆116Updated 6 years ago
- ☆71Updated last year
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- Easy Reduced Basis method☆88Updated 2 months ago
- ☆152Updated 3 years ago