globalchange  > 过去全球变化的重建
DOI: 10.2172/1177970
报告号: DOE-UCSD--SC0002349-Final
报告题名:
Parameter Estimation and Model Validation of Nonlinear Dynamical Networks
作者: Abarbanel, Henry; Gill, Philip
出版年: 2015
发表日期: 2015-03-31
总页数: 15
国家: 美国
语种: 英语
英文关键词: Data assimilation ; numerical weather prediction ; geosciences ; neurobiology of functional circuits
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: “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.
URL: http://www.osti.gov/scitech/servlets/purl/1177970
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资源类型: 研究报告
标识符: http://119.78.100.158/handle/2HF3EXSE/41908
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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Abarbanel, Henry,Gill, Philip. Parameter Estimation and Model Validation of Nonlinear Dynamical Networks. 2015-01-01.
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