PredictiveIntelligenceLab / USNCCM15-Short-Course-Recent-Advances-in-Physics-Informed-Deep-LearningLinks
☆117Updated 5 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
- ☆97Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆145Updated 5 years ago
- ☆210Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆92Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆63Updated 5 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆85Updated 4 years ago
- ☆180Updated 2 months ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 4 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆88Updated last year
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆259Updated last year
- ☆53Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆78Updated 2 years ago
- ☆141Updated 2 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆70Updated 2 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆149Updated 5 years ago
- ☆41Updated 5 years ago
- ☆67Updated last year
- hPINN: Physics-informed neural networks with hard constraints☆133Updated 3 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆135Updated 3 years ago
- Implementation of PINNs in TensorFlow 2☆78Updated last year
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆193Updated 2 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 4 years ago
- POD-PINN code and manuscript☆51Updated 6 months ago
- ☆165Updated last year
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆72Updated last month
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- PyTorch-FEniCS interface☆101Updated 4 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆56Updated 4 years ago