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Title:

Physics-informed neural networks for inverse problems in supersonic flows

Document type:
Zeitschriftenaufsatz
Author(s):
Jagtap, Ameya D.; Mao, Zhiping; Adams, Nikolaus; Karniadakis, George Em
Abstract:
Accurate solutions to inverse supersonic compressible flow problems are often required for designing specialized aerospace vehicles. In particular, we consider the problem where we have data available for density gradients from Schlieren photography as well as data at the inflow and part of the wall boundaries. These inverse problems are notoriously difficult, and traditional methods may not be adequate to solve such ill-posed inverse problems. To this end, we employ the physics-informed neural...     »
Keywords:
Extended physics-informed neural networks; Entropy conditions: Supersonic compressible flows; Inverse problems
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Journal of Computational Physics
Year:
2022
Journal volume:
466
Pages contribution:
111402
Covered by:
Scopus
Language:
en
Fulltext / DOI:
doi:10.1016/j.jcp.2022.111402
WWW:
https://www.sciencedirect.com/science/article/pii/S0021999122004648
Publisher:
Elsevier BV
E-ISSN:
0021-9991
Submitted:
23.02.2022
Accepted:
17.06.2022
Date of publication:
01.10.2022
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik
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