maziarraissi / HPM
Hidden physics models: Machine learning of nonlinear partial differential equations
☆144Updated 5 years ago
Alternatives and similar repositories for HPM:
Users that are interested in HPM are comparing it to the libraries listed below
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆279Updated 2 years ago
- ☆116Updated 5 years ago
- ☆62Updated 5 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- ☆175Updated last week
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆114Updated 3 years ago
- ☆132Updated 2 years ago
- ☆198Updated 3 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 4 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆92Updated 2 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆147Updated 3 months ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆77Updated 2 years ago
- ☆92Updated 3 years ago
- ☆64Updated last year
- Solving PDEs with NNs☆53Updated 2 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆187Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆87Updated last year
- Deep Learning of Vortex Induced Vibrations☆93Updated 5 years ago
- Deep learning for Engineers - Physics Informed Deep Learning☆335Updated last year
- ☆123Updated 2 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆258Updated last year
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆84Updated 4 years ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- PyTorch-FEniCS interface☆100Updated 4 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
- ☆41Updated 4 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆132Updated 3 years ago
- A place to share problems solved with SciANN☆275Updated last year
- ☆250Updated 2 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆147Updated 5 years ago