juansensio / nangs
Solving PDEs with NNs
☆43Updated last year
Related projects: ⓘ
- ☆113Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆43Updated 2 years ago
- ☆82Updated 2 years ago
- Example problems in Physics informed neural network in JAX☆69Updated last year
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆80Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆139Updated 4 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆69Updated 2 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆52Updated 2 years ago
- ☆37Updated 4 years ago
- ☆115Updated 5 years ago
- ☆168Updated 2 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆59Updated 4 years ago
- ☆40Updated 8 months ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆83Updated 2 years ago
- hPINN: Physics-informed neural networks with hard constraints☆109Updated 2 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆146Updated last year
- ☆60Updated 5 years ago
- ☆47Updated last year
- PyTorch-FEniCS interface☆97Updated 3 years ago
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆110Updated 2 years ago
- ☆150Updated 6 months ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆132Updated 4 years ago
- ☆50Updated 6 months ago
- Applications of PINOs☆105Updated last year
- A library for dimensionality reduction on spatial-temporal PDE☆58Updated 5 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆42Updated 4 years ago
- ☆91Updated 2 months ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆136Updated last year
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆59Updated last year
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆76Updated last year