rfarell / Reduced-Order-Modeling-TutorialsLinks
A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Easily runnable on Google Colab.
☆25Updated 5 months ago
Alternatives and similar repositories for Reduced-Order-Modeling-Tutorials
Users that are interested in Reduced-Order-Modeling-Tutorials are comparing it to the libraries listed below
Sorting:
- POD-PINN code and manuscript☆52Updated 8 months ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆75Updated last year
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆25Updated 3 years ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆48Updated last year
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆88Updated last year
- Multifidelity DeepONet☆34Updated 2 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆27Updated 2 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆29Updated last year
- ☆39Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆86Updated 4 years ago
- Physics-guided neural network framework for elastic plates☆42Updated 3 years ago
- PDE Preserved Neural Network☆53Updated 2 months ago
- gPINN: Gradient-enhanced physics-informed neural networks☆93Updated 3 years ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆35Updated 2 years ago
- DeepONet extrapolation☆27Updated 2 years ago
- Physics-informed neural networks for two-phase flow problems☆63Updated 2 months ago
- PINN in solving Navier–Stokes equation☆106Updated 5 years ago
- Examples implementing physics-informed neural networks (PINN) in Pytorch☆70Updated 3 years ago
- parallel PINNs; RANS equations; spatiotemporal parallel; PINNs☆32Updated last year
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆47Updated 2 years ago
- ☆54Updated 2 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆71Updated 2 years ago
- ☆72Updated 7 months ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆55Updated 3 years ago
- Non-adaptive and residual-based adaptive sampling for PINNs☆81Updated 2 years ago
- A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation☆29Updated last year
- Multi-fidelity reduced-order surrogate modeling☆24Updated 3 weeks ago
- Physics Informed Fourier Neural Operator☆23Updated 7 months ago
- Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems☆56Updated 4 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆49Updated 3 years ago