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

Reduced order modelling techniques for OpenFOAM

Data-driven reduced order modeling for nonlinear dynamical systems

FlowNet - Data-Driven Reservoir Predictions

The Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN

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

linear and non-linear solvers, time integration, roms

Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.

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

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.

ITHACA-SEM - In real Time Highly Advanced Computational Applications for Spectral Element Methods

The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.

Probabilistic Response mOdel Fitting with Interactive Tools

Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.

Matlab implementation of online and window dynamic mode decomposition algorithms

Standardized Non-Intrusive Reduced Order Modeling

Python Framework for data-driven model Order Reduction of multi-physiCs problEms

RBniCSx - reduced order modelling in FEniCSx

A GPU-accelerated toolbox for hyperbolic PDEs in a weaker (viscosity) sense. It leverages the integral to the solution of the conservation of momentum problem (being equivalent to the derivative of Hamilton-Jacobi equations) in one spatial dimension. We resolve such hyperbolic differential equations using wave-front propagating schemes on a spat…

Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems