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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Cubic flux; Machine learning; Undercompressive shocks; Truncation error; Convolutional neural network
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Horizon 2020:
grant agreement No. 667483
Journal title:
Journal of Computational Physics
Year:
2021
Journal volume:
437
Pages contribution:
110324
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.1016/j.jcp.2021.110324
WWW:
https://www.sciencedirect.com/science/article/pii/S0021999121002199#ab0020
Publisher:
Elsevier BV
E-ISSN:
0021-9991
Date of publication:
01.07.2021
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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