Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

  • Thomas Duriez
  • Steven L. Brunton
  • Bernd R. Noack

Part of the Fluid Mechanics and Its Applications book series (FMIA, volume 116)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 1-10
  3. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 11-48
  4. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 49-68
  5. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 69-91
  6. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 93-120
  7. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 121-152
  8. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 153-168
  9. Thomas Duriez, Steven L. Brunton, Bernd R. Noack
    Pages 169-187
  10. Back Matter
    Pages 189-211

About this book

Introduction

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.  

Keywords

textbook Fluid mechanics Machine learning Genetic programming Dynamical systems aerodynamic drag reduction control design MLC complex linear systems

Authors and affiliations

  • Thomas Duriez
    • 1
  • Steven L. Brunton
    • 2
  • Bernd R. Noack
    • 3
  1. 1.Laboratorio de Fluido DinámicaCONICET - Universidad de Buenos AiresBuenos AiresArgentina
  2. 2.Mechanical Engineering DepartmentUniversity of WashingtonSeattleUSA
  3. 3.Institut für Strömungsmechanik, Technische Universität BraunschweigDépartement Mécanique-Energétique, LIMSI-CNRS UPR 3251, Orsay, FranceBraunschweigGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-40624-4
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-40623-7
  • Online ISBN 978-3-319-40624-4
  • Series Print ISSN 0926-5112
  • Series Online ISSN 2215-0056
  • About this book
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