A library for dimensionality reduction on spatial-temporal PDE

Dimension reduced surrogate construction for parametric PDE maps

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

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

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.

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

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

Code TMA4900 Industrial Mathematics, Master’s Thesis

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.

Shallow Recurrent Decoder for Nuclear Reactors applications

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Neural network pruning to reduce the size of Neural Implicit Flow network.