skip to main content

Title: Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.
Authors:
 [1] ;  [1]
  1. Univ. of California, San Diego, CA (United States)
Publication Date:
OSTI Identifier:
1177970
Report Number(s):
DOE-UCSD--SC0002349-Final
DOE Contract Number:
SC0002349
Resource Type:
Technical Report
Research Org:
University of California, San Diego, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 58 GEOSCIENCES; 59 BASIC BIOLOGICAL SCIENCES; 42 ENGINEERING Data assimilation; numerical weather prediction; geosciences; neurobiology of functional circuits