TY - GEN
T1 - MIXED-VARIABLE GLOBAL SENSITIVITY ANALYSIS WITH APPLICATIONS TO DATA-DRIVEN COMBINATORIAL MATERIALS DESIGN
AU - Comlek, Yigitcan
AU - Wang, Liwei
AU - Chen, Wei
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the novel Latent Variable Gaussian Process (LVGP) with Sobol' analysis to develop the first metamodel-based mixed-variable GSA method. Through two analytical case studies, we first validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the new metamodel-based mixed-variable GSA method can benefit various engineering design applications, we employ our method with multi-objective Bayesian optimization (BO) to accelerate the Pareto front design exploration in many-level combinatorial design spaces. Specifically, we implement a sensitivity-aware design framework for metal-organic framework (MOF) materials that are constructed only from qualitative design variables and show the benefits of our method for expediting the exploration of novel MOF candidates from a many-level large combinatorial design space.
AB - Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the novel Latent Variable Gaussian Process (LVGP) with Sobol' analysis to develop the first metamodel-based mixed-variable GSA method. Through two analytical case studies, we first validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the new metamodel-based mixed-variable GSA method can benefit various engineering design applications, we employ our method with multi-objective Bayesian optimization (BO) to accelerate the Pareto front design exploration in many-level combinatorial design spaces. Specifically, we implement a sensitivity-aware design framework for metal-organic framework (MOF) materials that are constructed only from qualitative design variables and show the benefits of our method for expediting the exploration of novel MOF candidates from a many-level large combinatorial design space.
KW - Bayesian Optimization
KW - Global Sensitivity Analysis
KW - Latent Variable Gaussian Process
KW - Metamodels
KW - Mixed-Variable Design Spaces
UR - http://www.scopus.com/inward/record.url?scp=85170216545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85170216545&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-110756
DO - 10.1115/DETC2023-110756
M3 - Conference contribution
AN - SCOPUS:85170216545
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 49th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -