Non-intrusive reduced order modeling of nonlinear problems using neural networks

•A novel non-intrusive RB method coupling proper orthogonal decomposition with multi-layer perceptrons.•Numerical studies for the nonlinear Poisson equation and the incompressible steady Navier–Stokes equations.•Physical and geometrical parametrizations considered.•Good agreement with the finite ele...

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Veröffentlicht in:	Journal of computational physics Jg. 363; S. 55 - 78
Hauptverfasser:	Hesthaven, J.S., Ubbiali, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cambridge Elsevier Inc 15.06.2018 Elsevier Science Ltd
Schlagworte:	Artificial neural networks Computational fluid dynamics Computational physics Decoupling Dependence Driven cavity flow Fluid flow Hypercubes Incompressible flow Latin hypercube sampling Levenberg–Marquardt algorithm Mathematical models Multi-layer perceptron Multilayers Navier-Stokes equations Neural networks Non-intrusive reduced basis method Nonlinear equations Partial differential equations Poisson distribution Poisson equation Proper Orthogonal Decomposition Reduced order models Poisson equation Proper orthogonal decomposition Levenberg–Marquardt algorithm Driven cavity flow Multi-layer perceptron Non-intrusive reduced basis method
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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