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Auteur Steven L. Brunton (1984-....) |
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Titre : Data-driven science and engineering : machine learning, dynamical systems, and control Type de document : texte imprimé Auteurs : J. Nathan Kutz, Auteur ; Steven L. Brunton (1984-....), Auteur Editeur : Cambridge ; New York ; Melbourne [UK ; USA] : Cambridge University Press (CUP) Année de publication : 2019 Importance : 1 vol. (XXII-472 p.) Présentation : ill. en noir et en coul., couv. ill. Format : 27 cm ISBN/ISSN/EAN : 978-1-108-42209-3 Note générale : Part I. Dimensionality reduction and transforms : 1. Singular value decomposition - 2. Fourier and wavelets transforms - 3. Sparsity and compressed sensing - Part II.Machine learning and data analysis : 4. Regression and model selection - 5. Clustering and classification - 6. Neural networks and deep learning - Part III. Dynamics and control : 7. data-driven dynamical systems - 8. Linear control theory - 9. Balanced models for control - 10. Data-driven control - Part IV Reduced order models : 11. Reduced order models (ROMs) - 12. Interpolation for parametric ROMs .- PPN 240312996 Langues : Anglais (eng) Tags : Analyse des données Apprentissage automatique Analyse globale (mathématiques) Mathématiques de l'ingénieur Fourier, Transformations de Fourier, Séries de Engineering -- Data processing Science -- Data processing Mathematical analysis Fourier transformations Fourier series Index. décimale : 510.246 2 Mathématiques pour l'ingénieur Résumé : Les pages liminaires indiquent : "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art." Note de contenu : Bibliogr. p. 443-470. Glossaire p.436-442. Index p.471-472 Data-driven science and engineering : machine learning, dynamical systems, and control [texte imprimé] / J. Nathan Kutz, Auteur ; Steven L. Brunton (1984-....), Auteur . - Cambridge ; New York ; Melbourne (UK ; USA) : Cambridge University Press (CUP), 2019 . - 1 vol. (XXII-472 p.) : ill. en noir et en coul., couv. ill. ; 27 cm.
ISBN : 978-1-108-42209-3
Part I. Dimensionality reduction and transforms : 1. Singular value decomposition - 2. Fourier and wavelets transforms - 3. Sparsity and compressed sensing - Part II.Machine learning and data analysis : 4. Regression and model selection - 5. Clustering and classification - 6. Neural networks and deep learning - Part III. Dynamics and control : 7. data-driven dynamical systems - 8. Linear control theory - 9. Balanced models for control - 10. Data-driven control - Part IV Reduced order models : 11. Reduced order models (ROMs) - 12. Interpolation for parametric ROMs .- PPN 240312996
Langues : Anglais (eng)
Tags : Analyse des données Apprentissage automatique Analyse globale (mathématiques) Mathématiques de l'ingénieur Fourier, Transformations de Fourier, Séries de Engineering -- Data processing Science -- Data processing Mathematical analysis Fourier transformations Fourier series Index. décimale : 510.246 2 Mathématiques pour l'ingénieur Résumé : Les pages liminaires indiquent : "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art." Note de contenu : Bibliogr. p. 443-470. Glossaire p.436-442. Index p.471-472 Exemplaires
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Titre : Dynamic mode decomposition : data-driven modeling of complex systems Type de document : texte imprimé Auteurs : J. Nathan Kutz, Auteur ; Steven L. Brunton (1984-....), Auteur ; Bingni W. Brunton, Auteur ; Joshua L. Proctor Editeur : Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM) Année de publication : 2016 Collection : Other titles in applied mathematics num. 149 Importance : 1 vol. (XVI-234 p.) Présentation : couv. ill. en coul., ill. en coul. Format : 26 cm ISBN/ISSN/EAN : 978-1-61197-449-2 Note générale : PPN 199786771 Langues : Anglais (eng) Tags : Décomposition (mathématiques) Analyse mathématique Decomposition (Mathematics) Mathematical analysis Index. décimale : 510.246 2 Mathématiques pour l'ingénieur Résumé : Data-driven dynamical systems is a burgeoning field, connecting how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is the first book to address the DMD algorithm and present a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. By blending theoretical development, example codes, and applications, the theory and its many innovations and uses are showcased. The efficacy of the DMD algorithm is shown through the inclusion of example problems from engineering, physical sciences, and biological sciences, and the authors provide extensive MATLAB® code, data for intuitive examples of key methods, and graphical presentations. This book can therefore be used in courses that integrate data analysis with dynamical systems, and will be a useful resource for engineers and applied mathematicians. (source : 4e de couv.) Note de contenu : Bibliogr. p. 213-231. Glossaire. Index Dynamic mode decomposition : data-driven modeling of complex systems [texte imprimé] / J. Nathan Kutz, Auteur ; Steven L. Brunton (1984-....), Auteur ; Bingni W. Brunton, Auteur ; Joshua L. Proctor . - Philadelphia, Pa. : Society for Industrial and Applied Mathematics (SIAM), 2016 . - 1 vol. (XVI-234 p.) : couv. ill. en coul., ill. en coul. ; 26 cm. - (Other titles in applied mathematics; 149) .
ISBN : 978-1-61197-449-2
PPN 199786771
Langues : Anglais (eng)
Tags : Décomposition (mathématiques) Analyse mathématique Decomposition (Mathematics) Mathematical analysis Index. décimale : 510.246 2 Mathématiques pour l'ingénieur Résumé : Data-driven dynamical systems is a burgeoning field, connecting how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is the first book to address the DMD algorithm and present a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development. By blending theoretical development, example codes, and applications, the theory and its many innovations and uses are showcased. The efficacy of the DMD algorithm is shown through the inclusion of example problems from engineering, physical sciences, and biological sciences, and the authors provide extensive MATLAB® code, data for intuitive examples of key methods, and graphical presentations. This book can therefore be used in courses that integrate data analysis with dynamical systems, and will be a useful resource for engineers and applied mathematicians. (source : 4e de couv.) Note de contenu : Bibliogr. p. 213-231. Glossaire. Index Réservation
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Code-barres Cote Support Localisation Section Disponibilité Nom du donateur OCA-NI-010208 010208 Ouvrages / Books OCA Bib. Nice Mont-Gros NI-Sous sol-1-Ouvrages Sorti jusqu'au 18/11/2025
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 203959957Langues : 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 Réservation
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Code-barres Cote Support Localisation Section Disponibilité Nom du donateur OCA-NI-009714 009714 Ouvrages / Books OCA Bib. Nice Mont-Gros NI-Salle de lecture-Ouvrages Sorti jusqu'au 18/11/2025
