A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows
Numerical Analysis
2022-05-02 v3 Numerical Analysis
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.
Keywords
Cite
@article{arxiv.2104.02800,
title = {A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows},
author = {Pavel Gavrilenko and Bernard Haasdonk and Oleg Iliev and Mario Ohlberger and Felix Schindler and Pavel Toktaliev and Tizian Wenzel and Maha Youssef},
journal= {arXiv preprint arXiv:2104.02800},
year = {2022}
}
Comments
9 pages, 1 table; Previously this version appeared as arXiv:2110.12388v2 which was submitted as a new work by accident