A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows
Gavrilenko Pavel, Haasdonk Bernard, Iliev Oleg, Ohlberger Mario, Schindler Felix, Toktaliev Pavel, Wenzel Tizian, Youssef Maha
Research article (book contribution) | Peer reviewedAbstract
We present an integrated approach for the use of simulated data from full order discretization as well as projection-based Reduced Basis reduced order models for the training of machine learning approaches, in particular Kernel Methods, in order to achieve fast, reliable predictive models for the chemical conversion rate in reactive flows with varying transport regimes.
Details about the publication
Publisher: Lirkov Ivan, Margenov Svetozar
Book title: Large-Scale Scientific Computing
Page range: 378-386
Publishing company: Springer VDI Verlag
Place of publication: Cham
Title of series: Lecture Notes in Computer Science (LNCS)
Volume of series: 13127
Status: Published
Release year: 2022
Language in which the publication is written: English
ISBN: 978-3-030-97548-7
Link to the full text: https://doi.org/10.1007/978-3-030-97549-4_43
Keywords: reactive flow; model order reduction; machine learning
Authors from the University of Münster
| Ohlberger, Mario | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) Center for Nonlinear Science Center for Multiscale Theory and Computation |
| Schindler, Felix Tobias | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) Institute for Analysis and Numerics |