A library for dimensionality reduction on spatial-temporal PDE

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

Dimension reduced surrogate construction for parametric PDE maps

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.

One-dimensional unsteady compressible reacting flow simulation framework, designed for simple prototyping and testing of novel reduced-order model methods.

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

OpenFOAM examples for data-driven ML and ROM

A C++ Finite Element Analysis software based on the Jem-Jive library

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

Framework to learn effective dynamics and couple a macro scale simulator with a fast neural network latent propagator.

Reduced Order Models in a scikit-learn approach.

Non-intrusive reduced-order modeling with geometry-informed snapshots. Current based registration is applied to compute the diffeomorphism between snapshots.

Code TMA4900 Industrial Mathematics, Master’s Thesis

Shallow Recurrent Decoder for Nuclear Reactors applications

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

A Surrogate Modeling Framework for the Phase-Field Simulation.

Parallel-in-time integration of Neural ODEs with reduced basis approximation

Code for building a reduced-order model for the linear elasticity equation on a square in 2D for my specialization Project at NTNU.

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Code for performing sPOD based NN prediction for transport-dominated systems