Effective Design for Sobol Indices Estimation Based on Polynomial Chaos Expansions

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• 关键词：Design of experiment ; Sensitivity analysis ; Sobol indices ; Polynomial chaos expansions ; Active learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9653
• 期：1
• 页码：165-184
• 全文大小：3,081 KB
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• 作者单位：Evgeny Burnaev (17)
  Ivan Panin (17)
  Bruno Sudret (18)
  
  17. Kharkevich Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow, 127994, Russia
  18. Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland
  
• 丛书名：Conformal and Probabilistic Prediction with Applications
• ISBN：978-3-319-33395-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9653

文摘

Sobol’ indices are a common metric of dependency in sensitivity analysis. It is used as a measure of confidence of input variables influence on the output of the analyzed mathematical model. We consider a problem of selection of experimental design points for Sobol’ indices estimation. Based on the concept of D-optimality, we propose a method for constructing an adaptive design of experiments, effective for the calculation of Sobol’ indices from Polynomial Chaos Expansions. We provide a set of applications that demonstrate the efficiency of the proposed approach.