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Predicting the Future [electronic resource] : Completing Models of Observed Complex Systems / by Henry Abarbanel.

By: Abarbanel, Henry [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Understanding Complex Systems: Publisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XVI, 238 p. 97 illus., 91 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461472186.Subject(s): Physics | Neurosciences | Computer simulation | Physics | Statistical Physics, Dynamical Systems and Complexity | Complex Systems | Numerical and Computational Physics | Simulation and Modeling | NeurosciencesDDC classification: 621 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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