VincLee8188 / Physics-Informed-Neural-Networks-PyTorch
Implementation of physics informed neural networks with PyTorch
☆19Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for Physics-Informed-Neural-Networks-PyTorch
- Solving inverse problems using conditional invertible neural networks.☆33Updated 3 years ago
- ☆27Updated last year
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆38Updated last year
- ☆24Updated 2 years ago
- ☆37Updated last year
- ☆51Updated 2 years ago
- Physics Informed Neural Networks☆20Updated 4 years ago
- ☆38Updated 3 months ago
- ☆13Updated 7 months ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆15Updated 3 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆51Updated 3 months ago
- Practicum on Supervised Learning in Function Spaces☆32Updated 2 years ago
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆22Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆46Updated 3 years ago
- DeepONet extrapolation☆24Updated last year
- ☆48Updated 6 months ago
- This is the implementation of the RecFNO.☆16Updated last year
- Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems☆13Updated 5 months ago
- Pytorch implementation of Bayesian physics-informed neural networks☆38Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆61Updated last year
- Deep-learning iterative solver for the heterogeneous 2D Helmholtz equation☆27Updated last year
- Variational Neural Networks for the Solution of Partial Differential Equations☆8Updated 4 years ago
- Implementation of fast PINN optimization with RBA weights☆42Updated 3 weeks ago
- ☆50Updated last year
- ☆114Updated 2 years ago
- XPINN code written in TensorFlow 2☆26Updated last year
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆56Updated 2 years ago
- Physics-informed deep super-resolution of spatiotemporal data☆32Updated last year
- ☆23Updated 2 years ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆22Updated 6 months ago