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

By:

Brunton, Steven L

Contributor(s):

Kutz, Jose Nathan

Material type: Text

9781108422093

Subject(s):

DDC classification:

620.002 BRU

Contents:

Part I -- Dimensionality Reduction and Transforms - Part II -- Machine Learning and Data Analysis Part III -- Dynamics and Control - Part IV -- Reduced Order Models -

Summary: 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

List(s) this item appears in: New Arrivals April - June 2022

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	IIITDM Kurnool General Stacks		620.002 BRU (Browse shelf(Opens below))	Checked out	15.10.2024	0004614
Books	IIITDM Kurnool General Stacks	Non-fiction	620.002 BRU (Browse shelf(Opens below))	Available		0004605
Books	IIITDM Kurnool General Stacks	Non-fiction	620.002 BRU (Browse shelf(Opens below))	Available		0004606
Books	IIITDM Kurnool General Stacks	Non-fiction	620.002 BRU (Browse shelf(Opens below))	Available		0004607
Reference	IIITDM Kurnool Reference	Reference	620.002 BRU (Browse shelf(Opens below))	Not For Loan		0004608

Part I --
Dimensionality Reduction and Transforms - Part II --
Machine Learning and Data Analysis Part III --
Dynamics and Control - Part IV --
Reduced Order Models -

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

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