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.
☆26Updated 9 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☆54Updated last year
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆28Updated 2 years ago
- Physics-guided neural network framework for elastic plates☆47Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆85Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆92Updated 2 years ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆55Updated last year
- ☆42Updated 3 years ago
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆33Updated 3 years ago
- PDE Preserved Neural Network☆58Updated 6 months ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- MIONet: Learning multiple-input operators via tensor product☆38Updated 3 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated last year
- parallel PINNs; RANS equations; spatiotemporal parallel; PINNs☆33Updated last year
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆38Updated 2 years ago
- 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
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆34Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆74Updated 2 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆57Updated 3 years ago
- Multi-fidelity reduced-order surrogate modeling☆25Updated 4 months ago
- Physics-informed neural networks for two-phase flow problems☆69Updated last month
- Physics-Informed Neural Network (PINN) for Solving Coupled PDEs Governing Thermochemical Physics in Bi-Material Systems☆58Updated 4 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆100Updated 3 years ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆52Updated 2 years ago
- Use of Turbulence Model (Spalart-Allmaras) with PINNs for mean flow reconstruction☆11Updated last year
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆84Updated 2 months ago
- ☆54Updated 3 years ago
- Code accompanying "Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks", Maddu et al., 2021☆14Updated 4 years ago