Regression-Based Sensitivity Analysis and Robust Design

Guido Ridolfi () and Erwin Mooij ()
Additional contact information
Guido Ridolfi: Delft University of Technology
Erwin Mooij: Delft University of Technology

A chapter in Space Engineering, 2016, pp 303-336 from Springer

Abstract: Abstract This paper presents the Regression-Based global Sensitivity Analysis method (RBSA). It is an approach for quantitative, variance-based, sensitivity analysis of mathematical models used for design purposes. The method is based on the subdivision of the global variance into its components, due to the design-factor contributions, using general polynomial regression models. The performance of the RBSA is compared to other methods commonly used in engineering for computing sensitivity, namely, the method of Sobol’, the Fourier amplitude sensitivity test, the method of Morris, and the standardized regression coefficients. It was found that RBSA, under certain circumstances, provides very accurate results with a significant reduction of the number of required model evaluations. A test case, using the mathematical models of two subsystems of a spacecraft, demonstrates how RBSA facilitates the discovery and understanding of the effects of the design choices on the performance of the system.

Keywords: Computer-supported design; Decision making; System(s) design; Space systems; Global sensitivity analysis; Conceptual design (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-41508-6_12

Ordering information: This item can be ordered from
http://www.springer.com/9783319415086

DOI: 10.1007/978-3-319-41508-6_12

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().