Articles liés à Data-Driven Computational Methods: Parameter and Operator...

Data-Driven Computational Methods: Parameter and Operator Estimations - Couverture rigide

Harlim, John

 
9781108472470: Data-Driven Computational Methods: Parameter and Operator Estimations
Couverture rigide
ISBN 10 : 1108472478 ISBN 13 : 9781108472470
Editeur : Cambridge University Press, 2018

Synopsis

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.

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À propos de l'auteur

John Harlim is a Professor of Mathematics and Meteorology at the Pennsylvania State University. His research interests include data assimilation and stochastic computational methods. In 2012, he received the Frontiers in Computational Physics award from the Journal of Computational Physics for his research contributions on computational methods for modeling Earth systems. He has previously co-authored another book, Filtering Complex Turbulent Systems (Cambridge, 2012).

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Cambridge University Press
Date d'édition
2018
Langue
anglais
ISBN 10 
1108472478
ISBN 13 
9781108472470
Reliure
Relié
Nombre de pages
168
Coordonnées du fabricant
non disponible
Personne responsable
Avery
https://www.avery.fr/

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