Data Assimilation: Mathematical Concepts and Instructive Examples

Nonfiction, Science & Nature, Science, Physics, Mathematical Physics, Earth Sciences, Computers
Cover of the book Data Assimilation: Mathematical Concepts and Instructive Examples by Rodolfo Guzzi, Springer International Publishing
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Author: Rodolfo Guzzi ISBN: 9783319224107
Publisher: Springer International Publishing Publication: September 16, 2015
Imprint: Springer Language: English
Author: Rodolfo Guzzi
ISBN: 9783319224107
Publisher: Springer International Publishing
Publication: September 16, 2015
Imprint: Springer
Language: English

This book endeavours to give a concise contribution to understanding the data assimilation and related methodologies. The mathematical concepts and related algorithms are fully presented, especially for those facing this theme for the first time. 

The first chapter gives a wide overview of the data assimilation steps starting from Gauss' first methods to the most recent as those developed under the Monte Carlo methods.  The second chapter treats the representation of the physical  system as an ontological basis of the problem. The third chapter deals with the classical Kalman filter, while the fourth chapter deals with the advanced methods based on  recursive Bayesian Estimation. A special chapter, the fifth, deals with the possible applications, from the first Lorenz model, passing trough the biology and medicine up to planetary assimilation, mainly on Mars.

This book serves both teachers and college students, and other interested parties providing the algorithms and formulas to manage the data assimilation everywhere a dynamic system is present.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book endeavours to give a concise contribution to understanding the data assimilation and related methodologies. The mathematical concepts and related algorithms are fully presented, especially for those facing this theme for the first time. 

The first chapter gives a wide overview of the data assimilation steps starting from Gauss' first methods to the most recent as those developed under the Monte Carlo methods.  The second chapter treats the representation of the physical  system as an ontological basis of the problem. The third chapter deals with the classical Kalman filter, while the fourth chapter deals with the advanced methods based on  recursive Bayesian Estimation. A special chapter, the fifth, deals with the possible applications, from the first Lorenz model, passing trough the biology and medicine up to planetary assimilation, mainly on Mars.

This book serves both teachers and college students, and other interested parties providing the algorithms and formulas to manage the data assimilation everywhere a dynamic system is present.

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