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Autor(en): 
  • Bernd R. Noack
  • Thomas Duriez
  • Steven L. Brunton
  • Machine Learning Control ¿ Taming Nonlinear Dynamics and Turbulence 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  April 2018  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Algorithms & data structures / Applications of Nonlinear Dynamics and Chaos Theory / Artificial Intelligence / Automatic control engineering / B / Classical and Continuum Physics / Control and Systems Theory / Control engineering / Control Structures and Microprogramming / Dynamics & statics / engineering / Engineering Fluid Dynamics / Fluid mechanics / Fluid- and Aerodynamics / Fluids / Microprogramming / Nonlinear Optics / Nonlinear science / Statistical physics
    ISBN:  9783319821405 
    EAN-Code: 
    9783319821405 
    Verlag:  Springer International Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  #116 - Fluid Mechanics and Its Applications  
    Dimensionen:  H 235 mm / B 155 mm / D 12 mm 
    Gewicht:  408 gr 
    Seiten:  232 
    Zus. Info:  Paperback 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.  

      



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