1. Reduced basis methods: Success, limitations and future challenges;M Ohlberger;Proceedings of the Conference Algoritmy,2016
2. Convolutional neural networks for very low-dimensional LPV approximations of incompressible Navier-Stokes equations;J Heiland;Frontiers Appl. Math. Stat,2022
3. Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders;K Lee;J. Comput. Phys,2020
4. Model reduction in linear parametervarying models using autoencoder neural networks;S Z Rizvi;IEEE,2018
5. POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition;S Fresca;Comput. Methods Appl. Mech. Eng,2022