Convolutional Neural Networks for LPV-Approximations of Semi-discrete Navier-Stokes Equations

DOI10.5281/zenodo.6401954Zenodo6401954MaRDI QIDQ6697251

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 31 March 2022

Copyright license: No records found.

Description

A `python` module with * a dynamic setup of *Convolutional Neural Networks* in `PyTorch` * an interface to `FEniCS` to generate data from FEM simulations of flows and * a numerical realization of FEM norm in the training neural networks developed to design very low-dimensional LPV approximations of incompressible Navier-Stokes equations. These files contain the core module and the scripts that produce the numerical examples of the paper with doi:10.3389/fams.2022.879140 Benner, Heiland, Bahmani (2022): *Convolutional Neural Networks for Very Low-dimensional LPV Approximations of Incompressible Navier-Stokes Equations*

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