Predicting the Future
Subtitle: Completing Models of Observed Complex Systems
By: by Henry Abarbanel
Contributors: SpringerLink (Online service)
Series: Understanding Complex Systems
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 238
ISBN-13: 9781461472186
Subjects: Computer simulation; Neurosciences; Numerical and Computational Physics; Dynamical Systems and Complexity Statistical Physics; Complex Systems; Physics; Simulation and Modeling
Description: 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.
Table of contents: title: Preface; title: 1 An Overview; The Challenge of Complex Systems; title: 2 Examples as a Guide to the Issues; title: 3 General Formulation of Statistical Data Assimilation; title: 4 Evaluating the Path Integral; title: 5 Twin Experiments; title: 6 Analysis of Experimental Data.