| 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 |
|
Data-driven science and engineering (2019)