TY  - BOOK
AU  - Abarbanel,Henry
ED  - SpringerLink (Online service)
TI  - Predicting the Future: Completing Models of Observed Complex Systems 
T2  - Understanding Complex Systems,
SN  - 9781461472186
AV  - QC174.7-175.36 
U1  - 621 23
PY  - 2013///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Physics
KW  - Neurosciences
KW  - Computer simulation
KW  - System theory
KW  - Statistical physics
KW  - Dynamical systems
KW  - Statistical Physics, Dynamical Systems and Complexity
KW  - Complex Systems
KW  - Numerical and Computational Physics
KW  - Simulation and Modeling
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/978-1-4614-7218-6
ER  -