Predicting the Future

Completing Models of Observed Complex Systems
2013
ISBN: 978-1-4614-7217-9
Verlag: Springer

Buch, Englisch, 238 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 5089 g

Reihe: Understanding Complex Systems

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.

Zielgruppe

Research

Autoren/Hrsg.

Abarbanel, Henry

Fachgebiete

• Mathematik | Informatik Mathematik Geometrie Dynamische Systeme
• Geowissenschaften Geologie Meteorologie, Klimatologie
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme

Inhaltsverzeichnis

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.

Über Autor(innen)

Henry Abarbanel is a new member of the Springer Complexity Board. He is a Professor of Physics at UCSD in La Jolla, CA. http://neurograd.ucsd.edu/faculty/detail.php?id=19