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

Forschungsartikel (Buchbeitrag)

Zusammenfassung

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 zur Publikation

Herausgeber*innen: Lirkov Ivan, Margenov Svetozar
Buchtitel: Large-Scale Scientific Computing
Veröffentlichungsjahr: 2022
Verlag: Springer
ISBN: 978-3-030-97548-7
Sprache, in der die Publikation verfasst ist: Englisch
Veranstaltung: Cham
Link zum Volltext: https://doi.org/10.1007/978-3-030-97549-4_43