Surrogate modeling for sensitivity analysis of models with high-dimensional outputs

Min Li, Gaofeng Jia, Ruo Qian Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Sensitivity analysis provides important information on how the input uncertainty impacts the system output uncertainty. Typically, sensitivity analysis entails large number of system evaluations. For expensive system models with high-dimensional outputs, direct adoption of such models for sensitivity analysis poses significant computational challenges. To address these challenges, an efficient dimension reduction and surrogate based approach is proposed for efficient sensitivity analysis of expensive system models with high-dimensional outputs. As an example, the proposed approach is applied to investigate the sensitivity of peak water level over large coastal regions in San Francisco Bay with respect to the construction of levees at different counties under projected sea level rise.

Original languageEnglish
Title of host publicationICASP13 Proceedings
Place of PublicationSeoul
PublisherSeoul National University
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 26 May 2019
Event13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019 - Seoul, Korea, Republic of
Duration: 26 May 201930 May 2019

Conference

Conference13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019
CountryKorea, Republic of
CitySeoul
Period26/05/1930/05/19

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