lululxvi / hpinnLinks
hPINN: Physics-informed neural networks with hard constraints
☆141Updated 3 years ago
Alternatives and similar repositories for hpinn
Users that are interested in hpinn are comparing it to the libraries listed below
Sorting:
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆97Updated 3 years ago
- ☆147Updated 3 years ago
- ☆99Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆81Updated this week
- ☆172Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆207Updated 2 years ago
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆245Updated 3 years ago
- Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.☆151Updated 5 years ago
- ☆221Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆88Updated 2 years ago
- physics-informed neural network for elastodynamics problem☆146Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆262Updated last year
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆114Updated 3 weeks ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆152Updated last year
- Characterizing possible failure modes in physics-informed neural networks.☆138Updated 3 years ago
- Implementation of the Deep Ritz method and the Deep Galerkin method☆58Updated 5 years ago
- POD-PINN code and manuscript☆52Updated 9 months ago
- PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations☆108Updated 2 years ago
- Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube proble…☆184Updated 2 years ago
- Tutorials on deep learning, Python, and dissipative particle dynamics☆195Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆75Updated 3 years ago
- PINN in solving Navier–Stokes equation☆109Updated 5 years ago
- Implementation of PINNs in TensorFlow 2☆80Updated 2 years ago
- Generative Pre-Trained Physics-Informed Neural Networks Implementation☆101Updated this week