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Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (Fluid Mechanics and Its Applications, 116)
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About Machine Learning Control – Taming Nonlinear
Product Description This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube. From the Back Cover This is the first book on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube. About the Author Thomas Duriez is a tenured French researcher at CONICET, Buenos Aires, Argentina. He works on experimental, numerical and theoretical aspects of fluid mechanics focusing on flow control. He is part of the pioneer team developing Machine Learning Control for laminar and turbulent flows. He studied at ESPCI-ParisTech, the Paul Sabatier University and IMFT (Toulouse). After his PhD thesis at PMMH (Paris) on the control of separated flows, he had several postdoctoral appointments in academia and industry, including PMMH (Paris), LFD, LIMSI (Orsay, France) and PPRIME Institute (Poitiers, France) as part of the TUCOROM team. In Poitiers, he developed Machine Learning Control under the leadership of Bernd Noack. Steven L. Brunton is an Assistant Professor in Mechanical Engineering and an Adjunct Assistant Professor in Applied Mathematics at the University of Washington in Seattle. He graduated with a B.S. in Mathematics from the California Institute of Technology and received his PhD in Mechanical and Aerospace Engineering from Princeton University. Currently, his research interests include data-driven modeling and control of complex dynamical systems using machine learning, compressed sensing, uncertainty quantification, and equation-free modeling; Flow control of unsteady aerodynamics, transport phenomena, and turbulent mixing enhancement; Adaptive and robust control techniques for ener
