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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 reviewed

Abstract

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

Editors: Lirkov Ivan, Margenov Svetozar
Book title: Large-Scale Scientific Computing
Page range: 378-386
Publisher: 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
DOI: 10.1007/978-3-030-97549-4_43
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 (CMTC)
Schindler, Felix Tobias
Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger)
Institute for Analysis and Numerics

Projects the publication originates from

ML-MORE: Machine learning and model order reduction to predict the efficiency of catalytic filters. Subproject 1: Model Order Reduction (ML-MORE)
Duration: 01/04/2020 - 31/12/2023
Funded by: Federal Ministry of Research, Technology and Space
Type of project: Participation in federally funded joint project
Cluster of Excellence 2044 - Mathematics Münster: Dynamics – Geometry – Structure (EXC 2044)
Duration: 01/01/2019 - 31/12/2025 | 1st Funding period
Funded by: DFG - Cluster of Excellence
Type of project: Main DFG-project hosted at University of Münster
EXC 2044 - C2: Multi-scale phenomena and macroscopic structures
Duration: 01/01/2019 - 31/12/2025 | 1st Funding period
Funded by: DFG - Cluster of Excellence
Type of project: Subproject in DFG-joint project hosted at University of Münster