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

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*