Data-driven science and engineering: machine learning, dynamical systems, and control
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| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
[2022]
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| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 552-587 Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xxiv, 590 Seiten Illustrationen, Diagramme |
| ISBN: | 9781009098489 1009098489 |
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| 245 | 1 | 0 | |a Data-driven science and engineering |b machine learning, dynamical systems, and control |c Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington |
| 250 | |a Second edition | ||
| 264 | 1 | |a Cambridge |b Cambridge University Press |c [2022] | |
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Datensatz im Suchindex
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Contents Preface Acknowledgments Common Optimization Techniques, Equations, Symbols, and Acronyms Parti Dimensionality Reduction and Transforms 1 Singular Value Decomposition (SVD) 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 Fourier and Wavelet Transforms 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3 Overview Matrix Approximation Mathematical Properties and Manipulations Pseudo-Inverse, Least-Squares, and Regression Principal Component Analysis (PCA) Eigenfaces Example Truncation and Alignment Randomized Singular Value Decomposition Tensor Decompositions and A-Way Data Arrays Fourier Series and Fourier Transforms Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) Transforming Partial Differential Equations Gabor Transform and the Spectrogram Laplace Transform Wavelets and Multi-Resolution Analysis Two-Dimensional Transforms and Image Processing Sparsity and Compressed Sensing 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Sparsity and Compression Compressed Sensing Compressed Sensing Examples The Geometry of Compression Sparse Regression Sparse Representation Robust Principal Component Analysis (RPCA) Sparse Sensor Placement page ix xiv xv 1 3 3 7 12 16 23 28 35 40 46 53 53 63 70 76 81 85 87 97 97 101 105 109 113 117 120 123 v
vi Contents 131 Partii Machine Learning and Data Analysis 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 Classic Curve Fitting Nonlinear Regression and Gradient Descent Regression and Ax = b: Over- and Under-Determined Systems Optimization as the Cornerstone of Regression The Pareto Front and Lex Parsimoniae Model Selection: Cross-Validation Model Selection: Information Criteria 168 169 174 178 182 186 189 193 198 Clustering and Classification 5.1 Feature Selection and Data Mining 5.2 Supervised versus Unsupervised Learning 5.3 Unsupervised Learning: UMeans Clustering 5.4 Unsupervised Hierarchical Clustering: Dendrogram 5.5 Mixture Models and the Expectation-Maximization Algorithm 5.6 Supervised Learning and Linear Discriminants 5.7 Support Vector Machines (SVM) 5.8 Classification Trees and Random Forest 5.9 Top 10 Algorithms of Data Mining circa 2008 (Before the Deep Learning Revolution) 6 133 134 140 145 151 155 158 162 Regression and Model Selection Neural Networks and Deep Learning 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 Neural Networks: Single-Layer Networks Multi-Layer Networks and Activation Functions The Backpropagation Algorithm The Stochastic Gradient Descent Algorithm Deep Convolutional Neural Networks Neural Networks for Dynamical Systems Recurrent Neural Networks Autoencoders Generative Adversarial Networks (GANs) The Diversity of Neural Networks 203 208 209 214 219 222 224 228 233 236 240 242 Partili Dynamics and Control 251 7 253 254 260 275 286 296 Data-Driven Dynamical Systems 7.1 7.2 7.3 7.4 7.5 Overview, Motivations, and Challenges Dynamic Mode Decomposition (DMD)
Sparse Identification of Nonlinear Dynamics (SINDy) Koopman Operator Theory Data-Driven Koopman Analysis
Contents ՚ ì՛՝ .j ՛ար.1 -r։íí-'»"'ťTí«:t։* ՛*“ 'v՛ ֊֊.v ,-sx՛î vii "·*■!՝·ηì*v*։*"՛ —so^un.^usrigisnls-.-.^viíns^-^ívmiíWíí-Tm^iíeísmss^í^í^ííSiiiír.í^íT:·:՝^--’ 8 Linear Control Theory 8.1 Closed-Loop Feedback Control 8.2 Linear Time-Invariant Systems 8.3 Controllability and Observability 8.4 Optimal Full-State Control: Linear-Quadratic Regulator (LQR) 8.5 Optimal Full-State Estimation: the Kalman Filter 8.6 Optimal Sensor-Based Control: Linear-Quadratic Gaussian (LQG) 8.7 Case Study: Inverted Pendulum on a Cart 8.8 Robust Control and Frequency-Domain Techniques 311 312 317 322 328 332 335 336 346 9 Balanced Models for Control 9.1 Model Reduction and System Identification 9.2 Balanced Model Reduction 9.3 System Identification 360 360 361 375 Part IV Advanced Data-Driven Modeling and Control 387 10 Data-Driven Control 10.1 Model Predictive Control (MPC) 10.2 Nonlinear System Identification for Control 10.3 Machine Learning Control 10.4 Adaptive Extremum-Seeking Control 389 390 392 398 408 11 Reinforcement Learning 11.1 Overview and Mathematical Formulation 11.2 Model-Based Optimization and Control 11.3 Model-Free Reinforcement Learning and Q-Learning 11.4 Deep Reinforcement Learning 11.5 Applications and Environments 11.6 Optimal Nonlinear Control 419 419 426 429 436 440 444 12 Reduced-Order Models (ROMs) 12.1 Proper Orthogonal Decomposition (POD) for Partial Differential Equations 12.2 Optimal Basis Elements: the POD Expansion 12.3 POD and Soliton Dynamics 12.4 Continuous Formulation of POD 12.5 POD with Symmetries: Rotations and Translations 12.6 Neural Networks for Time-
Stepping with POD 12.7 Leveraging DMD and SINDy for Galerkin-POD 449 449 455 461 465 470 475 479 13 Interpolation for Parametric Reduced-Order Models 13.1 Gappy POD 13.2 Error and Convergence of Gappy POD 13.3 Gappy Measurements: Minimize Condition Number 13.4 Gappy Measurements: Maximal Variance 485 485 490 493 497
viii Contents 13.5 13.6 13.7 13.8 13.9 14 POD and the Discrete Empirical Interpolation Method (DEIM) DEIM Algorithm Implementation Decoder Networks for Interpolation Randomization and Compression for ROMs Machine Learning ROMs Physics-Informed Machine Learning 14.1 14.2 14.3 14.4 14.5 14.6 14.7 Mathematical Foundations SINDy Autoencoder: Coordinates and Dynamics Koopman Forecasting Learning Nonlinear Operators Physics-Informed Neural Networks (PINNs) Learning Coarse-Graining for PDEs Deep Learning and Boundary Value Problems Glossary References Index 500 504 508 512 513 520 520 523 526 529 533 535 539 542 552 588 |
| adam_txt |
Contents Preface Acknowledgments Common Optimization Techniques, Equations, Symbols, and Acronyms Parti Dimensionality Reduction and Transforms 1 Singular Value Decomposition (SVD) 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 Fourier and Wavelet Transforms 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3 Overview Matrix Approximation Mathematical Properties and Manipulations Pseudo-Inverse, Least-Squares, and Regression Principal Component Analysis (PCA) Eigenfaces Example Truncation and Alignment Randomized Singular Value Decomposition Tensor Decompositions and A-Way Data Arrays Fourier Series and Fourier Transforms Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) Transforming Partial Differential Equations Gabor Transform and the Spectrogram Laplace Transform Wavelets and Multi-Resolution Analysis Two-Dimensional Transforms and Image Processing Sparsity and Compressed Sensing 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Sparsity and Compression Compressed Sensing Compressed Sensing Examples The Geometry of Compression Sparse Regression Sparse Representation Robust Principal Component Analysis (RPCA) Sparse Sensor Placement page ix xiv xv 1 3 3 7 12 16 23 28 35 40 46 53 53 63 70 76 81 85 87 97 97 101 105 109 113 117 120 123 v
vi Contents 131 Partii Machine Learning and Data Analysis 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 5 Classic Curve Fitting Nonlinear Regression and Gradient Descent Regression and Ax = b: Over- and Under-Determined Systems Optimization as the Cornerstone of Regression The Pareto Front and Lex Parsimoniae Model Selection: Cross-Validation Model Selection: Information Criteria 168 169 174 178 182 186 189 193 198 Clustering and Classification 5.1 Feature Selection and Data Mining 5.2 Supervised versus Unsupervised Learning 5.3 Unsupervised Learning: UMeans Clustering 5.4 Unsupervised Hierarchical Clustering: Dendrogram 5.5 Mixture Models and the Expectation-Maximization Algorithm 5.6 Supervised Learning and Linear Discriminants 5.7 Support Vector Machines (SVM) 5.8 Classification Trees and Random Forest 5.9 Top 10 Algorithms of Data Mining circa 2008 (Before the Deep Learning Revolution) 6 133 134 140 145 151 155 158 162 Regression and Model Selection Neural Networks and Deep Learning 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 Neural Networks: Single-Layer Networks Multi-Layer Networks and Activation Functions The Backpropagation Algorithm The Stochastic Gradient Descent Algorithm Deep Convolutional Neural Networks Neural Networks for Dynamical Systems Recurrent Neural Networks Autoencoders Generative Adversarial Networks (GANs) The Diversity of Neural Networks 203 208 209 214 219 222 224 228 233 236 240 242 Partili Dynamics and Control 251 7 253 254 260 275 286 296 Data-Driven Dynamical Systems 7.1 7.2 7.3 7.4 7.5 Overview, Motivations, and Challenges Dynamic Mode Decomposition (DMD)
Sparse Identification of Nonlinear Dynamics (SINDy) Koopman Operator Theory Data-Driven Koopman Analysis
Contents ՚ ì՛՝ .j ՛ար.1 -r։íí-'»"'ťTí«:t։* ՛*“ 'v՛ ֊֊.v ,-sx՛î vii "·*■!՝·ηì*v*։*"՛ —so^un.^usrigisnls-.-.^viíns^-^ívmiíWíí-Tm^iíeísmss^í^í^ííSiiiír.í^íT:·:՝^--’ 8 Linear Control Theory 8.1 Closed-Loop Feedback Control 8.2 Linear Time-Invariant Systems 8.3 Controllability and Observability 8.4 Optimal Full-State Control: Linear-Quadratic Regulator (LQR) 8.5 Optimal Full-State Estimation: the Kalman Filter 8.6 Optimal Sensor-Based Control: Linear-Quadratic Gaussian (LQG) 8.7 Case Study: Inverted Pendulum on a Cart 8.8 Robust Control and Frequency-Domain Techniques 311 312 317 322 328 332 335 336 346 9 Balanced Models for Control 9.1 Model Reduction and System Identification 9.2 Balanced Model Reduction 9.3 System Identification 360 360 361 375 Part IV Advanced Data-Driven Modeling and Control 387 10 Data-Driven Control 10.1 Model Predictive Control (MPC) 10.2 Nonlinear System Identification for Control 10.3 Machine Learning Control 10.4 Adaptive Extremum-Seeking Control 389 390 392 398 408 11 Reinforcement Learning 11.1 Overview and Mathematical Formulation 11.2 Model-Based Optimization and Control 11.3 Model-Free Reinforcement Learning and Q-Learning 11.4 Deep Reinforcement Learning 11.5 Applications and Environments 11.6 Optimal Nonlinear Control 419 419 426 429 436 440 444 12 Reduced-Order Models (ROMs) 12.1 Proper Orthogonal Decomposition (POD) for Partial Differential Equations 12.2 Optimal Basis Elements: the POD Expansion 12.3 POD and Soliton Dynamics 12.4 Continuous Formulation of POD 12.5 POD with Symmetries: Rotations and Translations 12.6 Neural Networks for Time-
Stepping with POD 12.7 Leveraging DMD and SINDy for Galerkin-POD 449 449 455 461 465 470 475 479 13 Interpolation for Parametric Reduced-Order Models 13.1 Gappy POD 13.2 Error and Convergence of Gappy POD 13.3 Gappy Measurements: Minimize Condition Number 13.4 Gappy Measurements: Maximal Variance 485 485 490 493 497
viii Contents 13.5 13.6 13.7 13.8 13.9 14 POD and the Discrete Empirical Interpolation Method (DEIM) DEIM Algorithm Implementation Decoder Networks for Interpolation Randomization and Compression for ROMs Machine Learning ROMs Physics-Informed Machine Learning 14.1 14.2 14.3 14.4 14.5 14.6 14.7 Mathematical Foundations SINDy Autoencoder: Coordinates and Dynamics Koopman Forecasting Learning Nonlinear Operators Physics-Informed Neural Networks (PINNs) Learning Coarse-Graining for PDEs Deep Learning and Boundary Value Problems Glossary References Index 500 504 508 512 513 520 520 523 526 529 533 535 539 542 552 588 |
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| author | Brunton, Steven L. 1984- Kutz, J. Nathan |
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| author_facet | Brunton, Steven L. 1984- Kutz, J. Nathan |
| author_role | aut aut |
| author_sort | Brunton, Steven L. 1984- |
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| discipline | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV048217965 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:49:58Z |
| indexdate | 2024-11-11T11:02:24Z |
| institution | BVB |
| isbn | 9781009098489 1009098489 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033598744 |
| oclc_num | 1315745081 |
| open_access_boolean | |
| owner | DE-739 DE-384 DE-573 DE-20 DE-Aug4 DE-83 DE-861 DE-1043 DE-19 DE-BY-UBM DE-859 DE-188 DE-29T DE-B768 DE-634 DE-703 |
| owner_facet | DE-739 DE-384 DE-573 DE-20 DE-Aug4 DE-83 DE-861 DE-1043 DE-19 DE-BY-UBM DE-859 DE-188 DE-29T DE-B768 DE-634 DE-703 |
| physical | xxiv, 590 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Brunton, Steven L. 1984- Verfasser (DE-588)1125029617 aut Data-driven science and engineering machine learning, dynamical systems, and control Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington Second edition Cambridge Cambridge University Press [2022] xxiv, 590 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite 552-587 Hier auch später erschienene, unveränderte Nachdrucke Engineering / Data processing Science / Data processing Mathematical analysis Machine learning Ingénierie / Informatique Sciences / Informatique Analyse mathématique Apprentissage automatique Kontrolltheorie (DE-588)4032317-1 gnd rswk-swf Dynamisches System (DE-588)4013396-5 gnd rswk-swf Ingenieurwissenschaften (DE-588)4137304-2 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Naturwissenschaften (DE-588)4041421-8 gnd rswk-swf Angewandte Mathematik (DE-588)4142443-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Angewandte Mathematik (DE-588)4142443-8 s Datenanalyse (DE-588)4123037-1 s Naturwissenschaften (DE-588)4041421-8 s Ingenieurwissenschaften (DE-588)4137304-2 s DE-604 Dynamisches System (DE-588)4013396-5 s Kontrolltheorie (DE-588)4032317-1 s Kutz, J. Nathan Verfasser (DE-588)1045188867 aut Erscheint auch als Online-Ausgabe 9780191635878 Vorangegangen ist 1. Auflage 2019 978-1-108-42209-3 (DE-604)BV046156579 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033598744&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brunton, Steven L. 1984- Kutz, J. Nathan Data-driven science and engineering machine learning, dynamical systems, and control Engineering / Data processing Science / Data processing Mathematical analysis Machine learning Ingénierie / Informatique Sciences / Informatique Analyse mathématique Apprentissage automatique Kontrolltheorie (DE-588)4032317-1 gnd Dynamisches System (DE-588)4013396-5 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd Datenanalyse (DE-588)4123037-1 gnd Naturwissenschaften (DE-588)4041421-8 gnd Angewandte Mathematik (DE-588)4142443-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4032317-1 (DE-588)4013396-5 (DE-588)4137304-2 (DE-588)4123037-1 (DE-588)4041421-8 (DE-588)4142443-8 (DE-588)4193754-5 |
| title | Data-driven science and engineering machine learning, dynamical systems, and control |
| title_auth | Data-driven science and engineering machine learning, dynamical systems, and control |
| title_exact_search | Data-driven science and engineering machine learning, dynamical systems, and control |
| title_exact_search_txtP | Data-driven science and engineering machine learning, dynamical systems, and control |
| title_full | Data-driven science and engineering machine learning, dynamical systems, and control Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington |
| title_fullStr | Data-driven science and engineering machine learning, dynamical systems, and control Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington |
| title_full_unstemmed | Data-driven science and engineering machine learning, dynamical systems, and control Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington |
| title_short | Data-driven science and engineering |
| title_sort | data driven science and engineering machine learning dynamical systems and control |
| title_sub | machine learning, dynamical systems, and control |
| topic | Engineering / Data processing Science / Data processing Mathematical analysis Machine learning Ingénierie / Informatique Sciences / Informatique Analyse mathématique Apprentissage automatique Kontrolltheorie (DE-588)4032317-1 gnd Dynamisches System (DE-588)4013396-5 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd Datenanalyse (DE-588)4123037-1 gnd Naturwissenschaften (DE-588)4041421-8 gnd Angewandte Mathematik (DE-588)4142443-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Engineering / Data processing Science / Data processing Mathematical analysis Machine learning Ingénierie / Informatique Sciences / Informatique Analyse mathématique Apprentissage automatique Kontrolltheorie Dynamisches System Ingenieurwissenschaften Datenanalyse Naturwissenschaften Angewandte Mathematik Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033598744&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bruntonstevenl datadrivenscienceandengineeringmachinelearningdynamicalsystemsandcontrol AT kutzjnathan datadrivenscienceandengineeringmachinelearningdynamicalsystemsandcontrol |