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Predicting the Future: Completing Models of Observed Complex Systems de Henry Abarbanel

Predicting the Future: Completing Models of Observed Complex Systems: Understanding Complex Systems

Autor Henry Abarbanel
en Limba Engleză Hardback – 11 iun 2013
Through the development of an exact path integral for use in transferring information from observations to a model of the observed system, the author provides a general framework for the discussion of model building and evaluation across disciplines. Through many illustrative examples drawn from models in neuroscience, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model’s consistency with observations is explored.
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Specificații

ISBN-13: 9781461472179
ISBN-10: 1461472172
Pagini: 279
Ilustrații: XVI, 238 p. 97 illus., 91 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.5 kg
Ediția:2013
Editura: Springer
Colecția Springer
Seria Understanding Complex Systems

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

Preface.- 1 An Overview; The Challenge of Complex Systems.- 2 Examples as a Guide to the Issues.- 3 General Formulation of Statistical Data Assimilation.- 4 Evaluating the Path Integral.- 5 Twin Experiments.- 6 Analysis of Experimental Data.

Textul de pe ultima copertă

Predicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information from observations to a model of the observed system. Through many illustrative examples drawn from models in neuroscience, fluid dynamics, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is investigated.
Using highly instructive examples, the problems of data assimilation and the means to treat them are clearly illustrated. This book will be useful for students and practitioners of physics, neuroscience, regulatory networks, meteorology and climate science, network dynamics, fluid dynamics, and other systematic investigations of complex systems.

Caracteristici

Formulates long standing state and parameter estimation problems Explores numerous examples drawn from a broad interdisciplinary collection of scholarly subjects Proposes a universal approach with practical examples to bolster significant advances in solving the problems of model determination and parameter estimation Includes supplementary material: sn.pub/extras