fpichi / gca-romLinks
GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.
☆36Updated last month
Alternatives and similar repositories for gca-rom
Users that are interested in gca-rom are comparing it to the libraries listed below
Sorting:
- ☆55Updated 3 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 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…☆56Updated 9 months ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- POD-PINN code and manuscript☆54Updated 11 months ago
- MIONet: Learning multiple-input operators via tensor product☆38Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆25Updated 4 months ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 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…☆18Updated last year
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆33Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆74Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆92Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- ☆12Updated this week
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 9 months ago
- PDE Preserved Neural Network☆57Updated 5 months ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Competitive Physics Informed Networks☆31Updated last year
- This repository contains code, which was used to generate large-scale results in the HINTS paper.☆33Updated last year
- PINNs for 2D Incompressible Navier-Stokes Equation☆54Updated last year
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆62Updated 4 months ago
- Discontinuity Computing Using Physics-Informed Neural Network☆26Updated last year
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- ☆28Updated last year
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆25Updated 2 years ago
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics☆64Updated 3 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆26Updated 4 years ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆51Updated 2 years ago