Physics-informed neural networks for high-speed flows

In this work we investigate the possibility of using physics-informed neural networks (PINNs) to approximate the Euler equations that model high-speed aerodynamic flows. In particular, we solve both the forward and inverse problems in one-dimensional and two-dimensional domains. For the forward prob...

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Bibliographic Details
Published in:	Computer methods in applied mechanics and engineering Vol. 360; p. 112789
Main Authors:	Mao, Zhiping, Jagtap, Ameya D., Karniadakis, George Em
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.03.2020 Elsevier BV
Subjects:	Boundary conditions Conservation laws Density Discontinuity Domains Equations of state Euler equations Euler-Lagrange equation Forward problem Hidden fluid mechanics High speed Inverse problems Machine learning Neural networks Numerical methods Oblique shock waves Parameter identification Riemann problem Schlieren photography Velocity Conservation laws Riemann problem 65M70 Neural networks 74S25 Machine learning Hidden fluid mechanics Euler equations 35L65
ISSN:	0045-7825
Online Access:	Get full text
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