02898cam a22004577i 4500 20903207 OSt 20260413195442.0 190325t20182018enka b 001 0 eng d 2018275521 GBB8B9910 bnb 018921686 Uk 9781108472470 hardcover 1108472478 hardcover 9781108615136 electronic book 20903207 (OCoLC)1024086800 YDX eng YDX rda UPM EYM UKMGB OCLCQ OCLCF YDXIT DLC lccopycat QA276 .H365 2018 006.312 H.J.D 2018 23 Harlim, John, author. Data-driven computational methods : parameter and operator estimations / John Harlim, the Pennsylvania State University. Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2018. ©2018 xi, 158 pages : illustrations (some color) ; 26 cm text txt rdacontent unmediated n rdamedia volume nc rdacarrier Includes bibliographical references (pages 151-155) and index. 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.-- Source other than the Library of Congress. Mathematical statistics. Stochastic analysis. Computer science. Computer science. fast (OCoLC)fst00872451 Stochastic analysis. fast (OCoLC)fst01133499 Online version: Harlim, John. Data-driven computational methods. Cambridge, UK : Cambridge University Press, 2018 9781108615136 7 cbc copycat 2 ncip 20 y-gencatlg ddc BK 0 130 130 0 0 ddc 0 0 MAIN MAIN CS 2026-04-13 0 006.312 H.J.D 2018 1433 2026-04-13 19:55:02 2026-04-13 BK