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Catalogue Information
Field name	Details
Dewey Class	003.76
Title	Data-driven computational methods : parameter and operator estimations (M) / John Harlim
Author	Harlim, John
Publication	Cambridge : Cambridge University Press , 2018
Physical Details	xi, 158 pages : ill. ; 26 cm.
ISBN	9781108472470
Note	Online publication date:July 2018
Summary Note	Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study:
Links to Related Works	Subject References: Computer Science . Mathematical Statistics . Stochastic Analysis . Authors: Harlim, John . Classification: 003.76 .

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Barcode	Shelf Location	Collection	Volume Ref.	Branch	Status	Due Date
0000000044468	519.220 HAR	General		SISSA .	. Available .

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