PredictiveIntelligenceLab / USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-LearningLinks
☆116Updated 6 years ago
Alternatives and similar repositories for USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-Learning
Users that are interested in USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-Learning are comparing it to the libraries listed below
Sorting:
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆115Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆146Updated 5 years ago
- ☆98Updated 3 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 5 years ago
- ☆42Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Solving PDEs with NNs☆55Updated 2 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆63Updated 3 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- ☆63Updated 6 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- hPINN: Physics-informed neural networks with hard constraints☆142Updated 3 years ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆178Updated 4 years ago
- ☆221Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆261Updated last year
- ☆130Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- ☆189Updated 5 months ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆82Updated 3 weeks ago
- Example problems in Physics informed neural network in JAX☆81Updated 2 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆153Updated 8 months ago
- ☆258Updated 2 years ago
- ☆54Updated 2 years ago
- Gaussian process-based interpretable latent space dynamics identification through deep autoencoder☆35Updated last week
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆75Updated 3 years ago
- Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond☆58Updated 4 years ago