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Data-driven computational methods : parameter and operator estimations

Author: John Harlim
Publisher: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2018. ©2018
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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  Read more...
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Additional Physical Format: Online version:
Harlim, John.
Data-driven computational methods.
Cambridge, UK : Cambridge University Press, 2018
Document Type: Book
All Authors / Contributors: John Harlim
ISBN: 9781108472470 1108472478
OCLC Number: 1024086800
Description: xi, 158 pages : illustrations (some color) ; 26 cm
Contents: 1. Introduction; 2. Markov chain Monte Carlo; 3. Ensemble Kalman filters; 4. Stochastic spectral methods; 5. Karhunen-Loeve expansion; 6. Diffusion forecast; Appendix A. Elementary probability theory; Appendix B. Stochastic processes; Appendix C. Elementary differential geometry; References; Index.
Responsibility: John Harlim, the Pennsylvania State University.

Abstract:

The mathematics behind, and the practice of, computational methods that leverage data for modelling dynamical systems are described in this book. It will teach readers how to fit data on the assumed  Read more...

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