Reduced order modelling techniques for OpenFOAM

flowTorch - a Python library for analysis and reduced-order modeling of fluid flows

Easy Reduced Basis method

Modred main repository

Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network

Dimension reduced surrogate construction for parametric PDE maps

Pythonic spectral proper orthogonal decomposition

Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Available on doi.org/10.1016/j.cma.2021.114181.

Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10915-021-01462-7.

Python tools for non-intrusive reduced order modeling

Non-intrusive reduced order models using proper orthogonal decomposition (POD) and radial basis function (RBF) interpolation for shallow water equations

HAPOD - Hierarchical Approximate Proper Orthogonal Decomposition

A straightforward prototype implementation of the incremental POD to demonstrate its use in reduced order modeling.

Neural Ordinary Differential Equations for model order reduction of time-dependent PDEs

Deep-learning model for optimised proper orthogonal decomposition of non-linear, hyperbolic, parametric PDEs based on a pre-processing method of the full-order solutions

Implementation of the Finite Element Method (FEM) and the Proper Orthogonal Decomposition (POD) to study the heat equation

#fluid-simulation #navier-stokes

Non-intrusive Reduced Order Modeling package

Solving a non-linear elliptic problem via POD, PINN and POD-nn approaches