xgxgnpu / My-awesome-PINN-papersLinks
☆32Updated 3 years ago
Alternatives and similar repositories for My-awesome-PINN-papers
Users that are interested in My-awesome-PINN-papers are comparing it to the libraries listed below
Sorting:
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆26Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- Official Github Repository for paper "Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (…☆12Updated 2 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆15Updated last year
- ☆97Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆74Updated 3 years ago
- ☆36Updated 2 years ago
- ☆54Updated 2 years ago
- Neural Galerkin☆16Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆21Updated 3 years ago
- ☆29Updated 2 years ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated last year
- Bayesian optimized physics-informed neural network for parameter estimation☆32Updated 8 months ago
- ☆36Updated 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…☆41Updated 2 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated last year
- Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems☆13Updated last year
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 2 years ago
- ☆16Updated last year
- The public repository about our joint FINN research project☆38Updated 2 years ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆16Updated 4 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- ☆20Updated 2 months ago
- ☆37Updated last year
- Code for Mesh Transformer describes in the EAGLE dataset☆41Updated 5 months ago
- Code accompanying "Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks", Maddu et al., 2021☆11Updated 3 years ago
- Original implementation of fast PINN optimization with RBA weights☆57Updated 3 months ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆23Updated 2 years ago