An artificial neural network framework for reduced order modeling of transient flows

•An artificial neural network model is proposed for non-intrusive model order reduction of nonlinear systems.•It presents an equation-free approach for predictive reduced order modeling when the control parameter values vary.•The robustness of the model has been tested by considering the viscous Bur...

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Veröffentlicht in:	Communications in nonlinear science & numerical simulation Jg. 77; H. C; S. 271 - 287
Hauptverfasser:	San, Omer, Maulik, Romit, Ahmed, Mansoor
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.10.2019 Elsevier Science Ltd Elsevier
Schlagworte:	Artificial intelligence Artificial neural networks Burgers equation Computer simulation Convective flows Direct numerical simulation Evolution Fluid dynamics Forecasting Galerkin method Learning theory Machine learning Mathematical models Model reduction Neural networks Non-intrusive model order reduction Nonlinear dynamics Nonlinear equations Nonlinear systems Prediction models Projection model Proper Orthogonal Decomposition Reduced order modeling Reduced order models Robustness (mathematics) State variable Wave propagation Proper orthogonal decomposition Reduced order modeling Convective flows Non-intrusive model order reduction Artificial neural networks
ISSN:	1007-5704, 1878-7274
Online-Zugang:	Volltext
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