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  • 标题:Data-driven Solution of Stochastic Differential Equations Using Maximum Entropy Basis Functions ⁎
  • 本地全文:下载
  • 作者:Vedang M. Deshpande ; Raktim Bhattacharya
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:7234-7239
  • DOI:10.1016/j.ifacol.2020.12.556
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this paper we present a data-driven approach for uncertainty propagation. In particular, we consider stochastic differential equations with parametric uncertainty. Solution of the differential equation is approximated using maximum entropy (maxent) basis functions similar to polynomial chaos expansions. Maxent basis functions are derived from available data by maximization of information-theoretic entropy, therefore, there is no need to specify basis functions beforehand. We compare the proposed maxent based approach with existing methods.
  • 关键词:Keywordsuncertainty propagationchaos expansionmaximum entropystochastic differential equationsdata-driven models
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