Data-Driven Science and Engineering
Machine Learning, Dynamical Systems, and Control
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.Read more
- Provides in-depth examples paired with comprehensive, open-source code
- Features concise, digestible explanations of complex concepts and their applications
- Online supplements include homeworks, video lectures, and code and datasets in MATLAB® and Python
Reviews & endorsements
'This is a very timely, comprehensive and well written book in what is now one of the most dynamic and impactful areas of modern applied mathematics. Data science is rapidly taking center stage in our society. The subject cannot be ignored, either by domain scientists or by researchers in applied mathematics who intend to develop algorithms that the community will use. The book by Brunton and Kutz is an excellent text for a beginning graduate student, or even for a more advanced researcher interested in this field. The main theme seems to be applied optimization. The subtopics include dimensional reduction, machine learning, dynamics and control and reduced order methods. These were well chosen and well covered.' Stanley Osher, University of CaliforniaSee more reviews
'Professors Kutz and Brunton bring both passion and rigor to this most timely subject matter. Data analytics is the important topic for engineering in the twenty-first century and this book covers the far-reaching subject matter with clarity and code examples. Bravo!' Steve M. Legensky, Founder and General Manager, Intelligent Light
'Brunton and Kutz provide a lively and comprehensive treatise on machine learning and data mining algorithms as applied to physical systems arising in science and engineering and their control. They provide an abundance of examples and wisdom that will be of great value to students and practitioners alike.' Tim Colonius, California Institute of Technology
'This is a cleanly bound, compact book with medium weight coated paper and crisp text. There are many well-composed figures, most of them in color, with good explanatory captions, and sample code for almost all computational examples. While the code is for MATLAB, it is well commented and should not be too difficult to translate to Python or other computer languages … This is a fine book, and quite good for a first edition. It is clearly written with many examples and informative figures has a very useful bibliography and many good programming examples. I would use it for a course without reservation, and it has a permanent place on my bookshelf as a reference.' John Starrett, Mathematical Association of America Reviews
'Throughout, topics are discussed with theoretical depth and accompanied by a substantial bibliography. The authors also make use of software code snips.' R. S. Stansbury, Choice
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- Date Published: February 2019
- format: Adobe eBook Reader
- isbn: 9781108390187
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
Part I. Dimensionality Reduction and Transforms:
1. Singular value decomposition
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.
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