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arXiv · 2104.02800

A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows

Pavel Gavrilenko·Bernard Haasdonk·Oleg Iliev·Mario Ohlberger·Felix Schindler·Pavel Toktaliev·Tizian Wenzel·Maha Youssef
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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.

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Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, Maha Youssef. 2021-04-06. A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. https://doi.org/10.1007/978-3-030-97549-4_43

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