MJfadeaway / DASLinks
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
☆39Updated last year
Alternatives and similar repositories for DAS
Users that are interested in DAS are comparing it to the libraries listed below
Sorting:
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆43Updated 2 years ago
- POD-PINN code and manuscript☆57Updated last year
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 5 months ago
- ☆54Updated 3 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆36Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- ☆63Updated 6 years ago
- MIONet: Learning multiple-input operators via tensor product☆44Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Reproduce the first two numerical experiments(Pytorch)☆26Updated 5 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- ☆167Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- ☆25Updated 5 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆27Updated last year
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆19Updated 2 years ago
- Implementation of the Deep Ritz method and the Deep Galerkin method☆61Updated 5 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural ne…☆18Updated last year
- Fourier Neural Operators to solve for Allen Cahn PDE equations☆20Updated 4 years ago
- ☆110Updated 4 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆76Updated 2 years ago
- PDE Preserved Neural Network☆59Updated 8 months ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆59Updated 4 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆77Updated 9 months ago