DuktigYajie / TGPT-PINN
Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Networks (PINNs) and reduced basis methods (RBM) to the non- linear model reduction regime while maintaining the type of network structure and the unsupervised nature of its learning.
☆15Updated last year
Alternatives and similar repositories for TGPT-PINN:
Users that are interested in TGPT-PINN are comparing it to the libraries listed below
- ☆9Updated last year
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated last year
- Soving heat transfer problems using PINN with tf2.0☆19Updated 3 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- ☆12Updated this week
- POD-PINN code and manuscript☆51Updated 5 months ago
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated last year
- Multifidelity DeepONet☆31Updated last year
- A Self-Training Physics-Informed Neural Network for Partial Differential Equations☆21Updated last year
- Physics-informed neural networks for highly compressible flows 🧠🌊☆26Updated last year
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆30Updated 2 years ago
- ☆24Updated 3 months ago
- Data preprocess method on Physics-informed neural networks☆15Updated 2 months ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 3 weeks ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆25Updated last year
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆30Updated 3 years ago
- Multi-fidelity reduced-order surrogate modeling☆22Updated this week
- MIONet: Learning multiple-input operators via tensor product☆34Updated 2 years ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆18Updated 2 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 3 months ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆25Updated 3 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆30Updated last year
- Yet another PINN implementation☆20Updated 10 months ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆28Updated last year
- DeepONet extrapolation☆27Updated last year
- ☆14Updated 2 years ago
- Implementation of Physics-Informed Neural Networks for Computational Mechanics based on the DeepXDE package.☆37Updated this week
- POD and DMD decomposition of data from fluid dynamics. This work has been produced during my internship at the von Karman Institute for F…☆30Updated 4 years ago
- ☆8Updated 5 months ago