Adaptive design of experiments for sobol indices estimation based on quadratic metamodel

Evgeny Burnaev, Ivan Panin

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8 Цитирования (Scopus)

Аннотация

Sensitivity analysis aims to identify which input parameters of a given mathematical model are the most important. One of the well-known sensitivity metrics is the Sobol sensitivity index. There is a number of approaches to Sobol indices estimation. In general, these approaches can be divided into two groups: Monte Carlo methods and methods based on metamodeling. Monte Carlo methods have well-established mathematical apparatus and statistical properties. However, they require a lot of model runs. Methods based on metamodeling allow to reduce a required number of model runs, but may be difficult for analysis. In this work, we focus on metamodeling approach for Sobol indices estimation, and particularly, on the initial step of this approach — design of experiments. Based on the concept of D-optimality, we propose a method for construction of an adaptive experimental design, effective for calculation of Sobol indices from a quadratic metamodel. Comparison of the proposed design of experiments with other methods is performed.

Язык оригиналаАнглийский
Название основной публикацииStatistical Learning and Data Sciences - 3rd International Symposium, SLDS 2015, Proceedings
РедакторыAlexander Gammerman, Vladimir Vovk, Harris Papadopoulos
ИздательSpringer Verlag
Страницы86-95
Число страниц10
Том9047
ISBN (печатное издание)9783319170909
DOI
СостояниеОпубликовано - 2015
Опубликовано для внешнего пользованияДа
Событие3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015 - Egham, Великобритания
Продолжительность: 20 апр. 201523 апр. 2015

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том9047
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349

Конференция

Конференция3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015
Страна/TерриторияВеликобритания
ГородEgham
Период20/04/1523/04/15

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