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)


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

Publisher: Lirkov Ivan, Margenov Svetozar
Book title: Large-Scale Scientific Computing
Release year: 2022
Publishing company: Springer
ISBN: 978-3-030-97548-7
Language in which the publication is writtenEnglish
Event: Cham
Link to the full text: https://doi.org/10.1007/978-3-030-97549-4_43