Convolutional autoencoders, clustering, and POD for low-dimensional parametrization of flow equations
Author:
Funder
Deutsche Forschungsgemeinschaft
Publisher
Elsevier BV
Reference39 articles.
1. Reduced basis methods: success, limitations and future challenges;Ohlberger,2016
2. Convolutional neural networks for very low-dimensional LPV approximations of incompressible Navier-Stokes equations;Heiland;Front. Appl. Math. Stat.,2022
3. Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders;Lee;J. Comput. Phys.,2020
4. Model reduction in linear parameter-varying models using autoencoder neural networks;Rizvi,2018
5. POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition;Fresca;Comput. Methods Appl. Mech. Eng.,2022
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