An adaptive model hierarchy for data-augmented training of kernel models for reactive flow

Haasdonk B, Ohlberger M, Schindler F

Forschungsartikel in Sammelband (Konferenz)

Zusammenfassung

We consider machine-learning of time-dependent quantities of interest derived from solution trajectories of parabolic partial differential equations. For large-scale or long-time integration scenarios, where using a full order model (FOM) to generate sufficient training data is computationally prohibitive, we propose an adaptive hierarchy of intermediate Reduced Basis reduced order models (ROM) to augment the FOM training data by certified ROM training data required to fit a kernel model.

Details zur Publikation

Buchtitel: MATHMOD 2022 Discussion Contribution Volume
Veröffentlichungsjahr: 2022
ISBN: 978-3-901608-95-7
Sprache, in der die Publikation verfasst ist: Englisch
Veranstaltung: Vienna
Link zum Volltext: https://arxiv.org/abs/2110.12388