Physics-informed neural networks for inverse problems in supersonic flows

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...

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Vydané v:	Journal of computational physics Ročník 466; s. 111402
Hlavní autori:	Jagtap, Ameya D., Mao, Zhiping, Adams, Nikolaus, Karniadakis, George Em
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge Elsevier Science Ltd 01.10.2022
Predmet:	Aerospace engineering Aerospace vehicles Compressible flow Computational physics Density gradients Elastic waves Euler-Lagrange equation Inverse problems Neural networks Schlieren photography Shock waves Supersonic flow
ISSN:	0021-9991, 1090-2716
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