Data-driven science and engineering : machine learning, dynamical systems, and control / Steven L. Brunton, University of Washington, J. Nathan Kutz, University of Washington.

By:

Brunton, Steven L

Contributor(s):

Kutz, Jose Nathan

Material type: Text

TextDescription: pages cmISBN:

9781108422093 (hardback : alk. paper)

Subject(s):

DDC classification:

620.00285 BRU

Summary: "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"--

Tags from this library: No tags from this library for this title. Log in to add tags.

Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Futures Studies Processing Center	Dept. of Futures Studies	620.00285 BRU (Browse shelf(Opens below))	Available		DFS4351

Browsing Dept. of Futures Studies shelves, Shelving location: Processing Center Close shelf browser (Hides shelf browser)

Previous								Next
Previous	620.00151 AYY Uncertainty modeling and analysis in engineering and the sciences /	620.00151 TYA MATLAB and Simulink for engineers	620.0015197 RAO Engineering optimization : theory and practice	620.00285 BRU Data-driven science and engineering : machine learning, dynamical systems, and control /	620.00285 PAO Engineering analysis : interactive methods and programs with FORTRAN, QuickBASIC, MATLAB, and Mathematica /	620.00285 PRA Getting Started with MATLAB: A Quick Introduction for Scientists & Engineers/	620.0042 ADR Shape and structure, from engineering to nature /	Next

Includes bibliographical references and index.

"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"--

There are no comments on this title.

to post a comment.