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.
- Publication Date:
- OSTI Identifier:
- Report Number(s):
- DOE Contract Number:
- 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
- 97 MATHEMATICS AND COMPUTING; 58 GEOSCIENCES; 59 BASIC BIOLOGICAL SCIENCES; 42 ENGINEERING Data assimilation; numerical weather prediction; geosciences; neurobiology of functional circuits
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