XuhuiM / PPINNLinks
Demo code for PPINN paper: https://www.sciencedirect.com/science/article/pii/S0045782520304357
☆10Updated 5 years ago
Alternatives and similar repositories for PPINN
Users that are interested in PPINN are comparing it to the libraries listed below
Sorting:
- POD-PINN code and manuscript☆57Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆76Updated 3 years ago
- XPINN code written in TensorFlow 2☆28Updated 3 years ago
- A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural ne…☆19Updated last year
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆36Updated 4 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- ☆25Updated 5 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 5 months ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 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…☆30Updated last year
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆27Updated last year
- MIONet: Learning multiple-input operators via tensor product☆44Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆77Updated 2 years ago
- Physics-guided neural network framework for elastic plates☆50Updated 3 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆37Updated 2 months ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆25Updated 2 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆37Updated 2 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆21Updated 2 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆59Updated 4 years ago
- ☆93Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- ☆54Updated 3 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆107Updated 3 years ago
- Physics-informed neural networks for highly compressible flows 🧠🌊☆29Updated 2 years ago
- Code accompanying "Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks", Maddu et al., 2021☆14Updated 4 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆58Updated last year
- Physics-informed neural networks for two-phase flow problems☆74Updated 4 months ago