A Control Systems Toolbox for Julia

pyMOR - Model Order Reduction with Python

RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.

Data-driven model reduction library with an emphasis on large scale parallelism and linear subspace methods

MADS: Model Analysis & Decision Support

Easy Reduced Basis method

Modred main repository

Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.

AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)

sssMOR - Sparse State-Space and Model Order Reduction Toolbox

AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control

Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.

Numerical Implementation (Finite Difference) of the Pseudo-two-Dimensional Model for Lithium-ion Batteries

emgr -- EMpirical GRamian Framework

Support Vector Regression for Unsupervised Machine Learning

morgen - Model Order Reduction for Gas and Energy Networks

HAPOD - Hierarchical Approximate Proper Orthogonal Decomposition

AMORe-CMS (Automatic Model Order Reduction using Component Mode Syntesis) is MATLAB software that automatically performs physics-based model order reduction using component mode synthesis (CMS) on structural FE models made in COMSOL Multiphysics.

Python bindings to pressio

Matlab implementation of online and window dynamic mode decomposition algorithms