Reduced order modelling techniques for OpenFOAM
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Updated
Apr 23, 2024 - C++
Reduced order modelling techniques for OpenFOAM
flowTorch - a Python library for analysis and reduced-order modeling of fluid flows
Modred main repository
Easy Reduced Basis method
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.
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
Python tools for non-intrusive reduced order modeling
Neural Ordinary Differential Equations for model order reduction of time-dependent PDEs
A straightforward prototype implementation of the incremental POD to demonstrate its use in reduced order modeling.
Implementation of the Finite Element Method (FEM) and the Proper Orthogonal Decomposition (POD) to study the heat equation
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
#fluid-simulation #navier-stokes
Non-intrusive Reduced Order Modeling package
The projects hooked with this specific repository are purely coded with python are developer-friendly and consist of prototype projects. This is just a start-up repository and is not supposed to be handling complete, descriptive projects.
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