| Titre : |
Machine Learning Control : Taming Nonlinear Dynamics and Turbulence. |
| Type de document : |
texte imprimé |
| Auteurs : |
Thomas Duriez, Auteur ; Steven L. Brunton (1984-....), Auteur ; Bernd R. Noack, Auteur ; Bernd R. Noack |
| Editeur : |
Berlin ; Heidelberg ; Dordrecht ; New York ; London ; Paris ; Wien : Springer Verlag |
| Année de publication : |
2017 |
| Collection : |
Fluid mechanics and its applications num. 116 |
| Importance : |
1 vol. (XX-211 p.) |
| Présentation : |
ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-3-319-40623-7 |
| Note générale : |
Sommaire : Introduction (Pages 1-10).- Machine Learning Control (MLC)(p.11-48). - Methods of Linear Control Theory (p. 49-68) - Benchmarking MLC Against Linear Control (p.69-91). - Taming Nonlinear Dynamics with MLC (p. 93-120). - Taming Real World Flow Control Experiments with MLC (p. 121-152). - MLC Tactics and Strategy (p.153-168). - Future Developments (p.169-187)
PPN 203959957 |
| Langues : |
Anglais (eng) |
| Tags : |
Fluides, Mécanique des Turbulence Apprentissage automatique Apprentissage machine Commande, Théorie de la Fluid mechanics Machine learning Control theory |
| Index. décimale : |
532 Mécanique des fluides. Mécanique des liquides |
| Résumé : |
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 |
| Note de contenu : |
Glossaire p. 189-194. Bibliogr. p.197. Index p.209-211 |
Machine Learning Control : Taming Nonlinear Dynamics and Turbulence. [texte imprimé] / Thomas Duriez, Auteur ; Steven L. Brunton (1984-....), Auteur ; Bernd R. Noack, Auteur ; Bernd R. Noack . - Berlin ; Heidelberg ; Dordrecht ; New York ; London ; Paris ; Wien : Springer Verlag, 2017 . - 1 vol. (XX-211 p.) : ill. ; 24 cm. - ( Fluid mechanics and its applications; 116) . ISBN : 978-3-319-40623-7 Sommaire : Introduction (Pages 1-10).- Machine Learning Control (MLC)(p.11-48). - Methods of Linear Control Theory (p. 49-68) - Benchmarking MLC Against Linear Control (p.69-91). - Taming Nonlinear Dynamics with MLC (p. 93-120). - Taming Real World Flow Control Experiments with MLC (p. 121-152). - MLC Tactics and Strategy (p.153-168). - Future Developments (p.169-187)
PPN 203959957 Langues : Anglais ( eng)
| Tags : |
Fluides, Mécanique des Turbulence Apprentissage automatique Apprentissage machine Commande, Théorie de la Fluid mechanics Machine learning Control theory |
| Index. décimale : |
532 Mécanique des fluides. Mécanique des liquides |
| Résumé : |
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 |
| Note de contenu : |
Glossaire p. 189-194. Bibliogr. p.197. Index p.209-211 |
|
Machine Learning Control (2017)