Predicting the temporal dynamics of turbulent channels through deep learning-Reference-Cited by-同舟云学术

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Predicting the temporal dynamics of turbulent channels through deep learning

Published:2022-08 Issue: Volume:96 Page:109010
ISSN:0142-727X
Container-title:International Journal of Heat and Fluid Flow
language:en
Short-container-title:International Journal of Heat and Fluid Flow

Author:

Borrelli Giuseppe,Guastoni Luca,Eivazi Hamidreza,Schlatter Philipp,Vinuesa Ricardo

Funder

Science and Engineering Research Council

Swedish e-Science Research Centre

Göran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning

Publisher

Elsevier BV

Subject

Fluid Flow and Transfer Processes,Mechanical Engineering,Condensed Matter Physics

Reference42 articles.

1. Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the koopman operator;Arbabi;SIAM J. Appl. Dyn. Syst.,2017

2. The proper orthogonal decomposition in the analysis of turbulent flows;Berkooz;Annu. Rev. Fluid Mech.,1993

3. Discovering governing equations from data by sparse identification of nonlinear dynamical systems;Brunton;PANS,2016

4. Chaos as an intermittently forced linear system;Brunton;Nat. Commun.,2017

5. Machine learning for fluid mechanics;Brunton;Annu. Rev. Fluid Mech.,2020

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