Big data and machine learning are driving profound technological progress across nearly every industry, and are rapidly shaping fluid mechanics research. This is a self-contained and pedagogical treatment of the data-driven tools that are leading research in model-order reduction, system identification, flow control, and turbulence closures.
Big data and machine learning are driving profound technological progress across nearly every industry, and are rapidly shaping fluid mechanics research. This is a self-contained and pedagogical treatment of the data-driven tools that are leading research in model-order reduction, system identification, flow control, and turbulence closures.
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. Jiménez; 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. Görtz, 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. Jiménez; References.
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. Jiménez; 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. Görtz, 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. Jiménez; References.
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