ehsankharazmi / hp-VPINNsLinks
hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations
☆84Updated last month
Alternatives and similar repositories for hp-VPINNs
Users that are interested in hp-VPINNs are comparing it to the libraries listed below
Sorting:
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆74Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆91Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- physics-informed neural network for elastodynamics problem☆146Updated 3 years ago
- POD-PINN code and manuscript☆53Updated 11 months ago
- ☆151Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- MIONet: Learning multiple-input operators via tensor product☆38Updated 2 years ago
- ☆98Updated 3 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆98Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- ☆112Updated 8 months ago
- hPINN: Physics-informed neural networks with hard constraints☆144Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks☆89Updated 2 years ago
- Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.☆151Updated 5 years ago
- ☆52Updated 10 months ago
- Physics-guided neural network framework for elastic plates☆46Updated 3 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- ☆54Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 8 months ago
- PINN in solving Navier–Stokes equation☆113Updated 5 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆42Updated 2 years ago
- Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems☆58Updated 4 years ago
- PINN program for computational mechanics☆121Updated last year
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆33Updated 3 years ago