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.

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

Modred main repository

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

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.

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

GDM is a python package containing pydantic data models for distribution power system assets and datasets. This package is actively being developed and maintained at National Renewable Energy Laboratory (NREL).

Nonlinear model reduction for operator learning

System-Theoretic Model Order Reduction in Python

Supplemental Material for "BUQEYE Guide to Projection-Based Emulators in Nuclear Physics"

Source code for the paper "Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inference" by McQuarrie, Khodabakhshi, and Willcox

Semantic Segmentation with reduced fully convolutional networks for higher latency and lower memory requirement.

SR-OpInf: symmetry-reduced model reduction via operator inference

Auxiliary tools: distributed SVD, QR, sample mesh, etc

Python implementation of the shifted proper orthogonal decomposition

Model Reduction of the Approximate Master Equation for Epidemic Processes on Complex Networks