jx-wang-s-group / ppnnLinks
PDE Preserved Neural Network
☆59Updated 7 months ago
Alternatives and similar repositories for ppnn
Users that are interested in ppnn are comparing it to the libraries listed below
Sorting:
- MIONet: Learning multiple-input operators via tensor product☆42Updated 3 years ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆54Updated 2 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆57Updated 3 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆37Updated 2 years ago
- ☆54Updated 3 years ago
- POD-PINN code and manuscript☆57Updated last year
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆69Updated last month
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆75Updated 8 months ago
- ☆66Updated 4 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…☆27Updated 11 months ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last month
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated 11 months ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Updated 4 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆19Updated last year
- Physics Informed Fourier Neural Operator☆26Updated last year
- ☆33Updated 11 months ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆40Updated 8 months ago
- Multi-fidelity reduced-order surrogate modeling☆28Updated 6 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆93Updated 2 years ago
- Physics-encoded recurrent convolutional neural network☆48Updated 3 years ago
- Original implementation of fast PINN optimization with RBA weights☆68Updated 3 months ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆33Updated 2 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆19Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆86Updated 4 months ago
- ☆47Updated last week
- This is the implementation of the RecFNO.☆25Updated 2 years ago