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Sensitivity Prewarping for Local Surrogate Modeling

In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input–output relationships. However, narrowing focus to local scale means that global trends must be relearned over and over again. In this article, we propose a framework for incorporating information from a global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the “warped simulator” is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.

Ketersediaan

Informasi Detail

Judul Seri

TECHNOMETRICS; Vol.64 No.4 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 535-547

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

SENSITIVITY ANALYSIS
COMPUTER EXPERIMENTS
DIMENSION REDUCTION
GAUSSIAN PROCESS
EMULATION
ACTIVE SUBSPACE
SUBBAGGING

Info Detail Spesifik

https://doi.org/10.1080/00401706.2022.2046170

Pernyataan Tanggungjawab

Nathan Wycoff, Mickael Binois, Robert B. Gramacy

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