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Titel:

A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Bezgin, Deniz A.; Schmidt, Steffen J.; Adams, Nikolaus A.
Abstract:
We propose a data-driven physics-informed finite-volume scheme for the approximation of small-scale dependent shocks. Nonlinear hyperbolic conservation laws with non-convex fluxes allow nonclassical shock wave solutions. In this work, we consider the cubic scalar conservation law as representative of such systems. As standard numerical schemes fail to approximate nonclassical shocks, schemes with controlled dissipation and schemes with well-controlled dissipation have been introduced by LeFloch...     »
Stichworte:
Cubic flux; Machine learning; Undercompressive shocks; Truncation error; Convolutional neural network
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Horizon 2020:
grant agreement No. 667483
Zeitschriftentitel:
Journal of Computational Physics
Jahr:
2021
Band / Volume:
437
Seitenangaben Beitrag:
110324
Nachgewiesen in:
Scopus
Sprache:
en
Volltext / DOI:
doi:10.1016/j.jcp.2021.110324
WWW:
https://www.sciencedirect.com/science/article/pii/S0021999121002199#ab0020
Verlag / Institution:
Elsevier BV
E-ISSN:
0021-9991
Publikationsdatum:
01.07.2021
TUM Einrichtung:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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