A library for dimensionality reduction on spatial-temporal PDE
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Updated
Apr 8, 2024 - Jupyter Notebook
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
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
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.
Neural network pruning to reduce the size of Neural Implicit Flow network.
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