Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

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Ta kontakt med Kundesenteret. Avbryt Send e-post. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence | myxalyleby.gq

Les mer. Om boka This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems.

P4: Machine Learning for PID Control

This machine learning control MLC is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed.

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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.

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Glauser, D. Williams and machine learning M.


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Schoenauer for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube. Skip to main content Skip to table of contents. Advertisement Hide.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Brunton Bernd R. Front Matter Pages i-xx.

Table of contents

Thomas Duriez, Steven L. Brunton, Bernd R. Pages Methods of Linear Control Theory.



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