Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

✍ Scribed by Steven L. Brunton, J. Nathan Kutz

• Publisher: Cambridge University Press
• Year: 2019
• Tongue: English
• Leaves: 496
• Category: Library

✦ Synopsis

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling 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.

✦ Table of Contents

Cover
Front Matter
Data-Driven Science and Engineering:
Machine Learning, Dynamical Systems, and Control
Copyright
Contents
Preface
Common Optimization Techniques, Equations,
Symbols, and Acronyms
Part I: Dimensionality Reduction and
Transforms
1 Singular Value Decomposition (SVD)
2 Fourier and Wavelet 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
Glossary
Bibliography
Index