juansensio / nangs
Solving PDEs with NNs
☆53Updated 2 years ago
Alternatives and similar repositories for nangs
Users that are interested in nangs are comparing it to the libraries listed below
Sorting:
- Sparse Physics-based and Interpretable Neural Networks☆49Updated 3 years ago
- ☆63Updated 5 years ago
- ☆116Updated 5 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆92Updated 3 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 4 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆145Updated 5 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆69Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆85Updated 4 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆79Updated 2 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆88Updated last year
- ☆95Updated 3 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆150Updated 4 months ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆68Updated 4 years ago
- ☆138Updated 2 years ago
- ☆41Updated 5 years ago
- Applications of PINOs☆125Updated 2 years ago
- DeepONet extrapolation☆27Updated last year
- ☆67Updated last year
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆114Updated 3 years ago
- Example problems in Physics informed neural network in JAX☆80Updated last year
- PyTorch-FEniCS interface☆100Updated 4 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆149Updated 5 years ago
- ☆21Updated 4 years ago
- hPINN: Physics-informed neural networks with hard constraints☆132Updated 3 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆92Updated 6 years ago
- Multifidelity DeepONet☆33Updated last year
- ☆52Updated 2 years ago
- ☆53Updated 2 years ago
- Dimension reduced surrogate construction for parametric PDE maps☆37Updated last month