Data-Driven Fluid Mechanics

Part I. Motivation: 1. Analysis, modeling and control of the cylinder wake B. R. Noack, A. Ehlert, C. N. Nayeri and M. Morzynski; 2. Coherent structures in turbulence: a data science perspective J. Jimenez; 3. Machine learning in fluids: pairing methods with problems S. Brunton; Part II. Methods from Signal Processing: 4. Continuous and discrete LTI systems M. A. Mendez; 5. Time-frequency analysis and wavelets S. Discetti; Part III. Data-Driven Decompositions: 6. The proper orthogonal decomposition S. Dawson; 7. The dynamic mode decomposition: from Koopman theory to applications P. J. Schmid; 8. Generalized and multiscale modal analysis M. A. Mendez; 9. Good practice and applications of data-driven modal analysis A. Ianiro; Part IV. Dynamical Systems: 10. Linear dynamical systems and control S. Dawson; 11. Nonlinear dynamical systems S. Brunton; 12. Methods for system identification S. Brunton; 13. Modern tools for the stability analysis of fluid flows P. J. Schmid; Part V. Applications: 14. Machine learning for reduced-order modeling B. R. Noack, D. Fernex and R. Semaan; 15. Advancing reacting flow simulations with data-driven models K. Zdybal, G. D'Alessio, G. Aversano, M. R. Malik, A. Coussement, J. C. Sutherland and A. Parente; 16. Reduced-order modeling for aerodynamic applications and multidisciplinary design optimization S. Goertz, P. Bekemeyer, M. Abu-Zurayk, T. Franz and M. Ripepi; 17. Machine learning for turbulence control B. R. Noack, G. Y. Cornejo Maceda, F. Lusseyran; 18. Deep reinforcement learning applied to active flow control J. Rabault and A. Kuhnle; Part VI. Perspectives: 19. The Computer as scientist J. Jimenez; References.

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• Mecânica dos fluidos