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Document type:
Zeitschriftenaufsatz 
Author(s):
Bezgin, Deniz A.; Schmidt, Steffen J.; Adams, Nikolaus A. 
Title:
A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks 
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 
Publisher:
Elsevier BV 
E-ISSN:
0021-9991 
Date of publication:
01.07.2021 
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik