YihaoHu / Neural_PDELinks
☆13Updated 3 years ago
Alternatives and similar repositories for Neural_PDE
Users that are interested in Neural_PDE are comparing it to the libraries listed below
Sorting:
- ☆54Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 4 months ago
- hPINN: Physics-informed neural networks with hard constraints☆152Updated 4 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- MIONet: Learning multiple-input operators via tensor product☆43Updated 3 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
- POD-PINN code and manuscript☆57Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆38Updated 2 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆36Updated 3 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆59Updated 3 years ago
- ☆195Updated last year
- ☆50Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations☆39Updated last year
- ☆54Updated last year
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆131Updated last month
- ☆163Updated 3 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆107Updated 3 years ago
- physics-informed neural network for elastodynamics problem☆153Updated 3 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
- Implementation of Fourier Neural Operator using PyTorch☆26Updated 2 months ago
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆95Updated 3 years ago
- ☆29Updated last year
- ☆117Updated 11 months ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Updated 4 years ago
- 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
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated last year
- ☆13Updated 2 years ago
- Implementation of the Deep Ritz method and the Deep Galerkin method☆61Updated 5 years ago