Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

GSTDTAP > 地球科学

DOI	10.2172/1177970
报告编号	DOE-UCSD--SC0002349-Final
来源ID	OSTI ID: 1177970
	Parameter Estimation and Model Validation of Nonlinear Dynamical Networks
	Abarbanel, Henry; Gill, Philip
	2015-03-31
出版年	2015
页数	15
语种	英语
国家	美国
领域	地球科学
英文摘要	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: âPredicting the Future: Completing Models of Observed Complex Systemsâ by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.
英文关键词	Data assimilation numerical weather prediction geosciences neurobiology of functional circuits
URL	查看原文
来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/6994
专题	地球科学
推荐引用方式 GB/T 7714	Abarbanel, Henry,Gill, Philip. Parameter Estimation and Model Validation of Nonlinear Dynamical Networks,2015.