2009/01/01 by Silvia Poles, Poles, Silvia, Alberto Lovison +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Latin Hypercube #Monte Carlo #Multiobjective Robust Design #Optimal Experimental Design Methods #Polynomial Chaos #Probabilistic and Robust Engineering Design #Uncertainty Quantification
paper · doi:10.4230/dagsemproc.09041.7
openalex publication_date 2009/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that the use of PCE may help and speed up the optimization process if compared to standard approaches such as Monte Carlo and Latin Hypercube sampling.