Robust system design with limited experimental data and an inexact simulation model

Wenbo Sun, Matthew Plumlee, Jingwen Hu, Jionghua Judy Jin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Computer simulations of physical systems are commonly used to improve system performance via optimizing the system design. We study the case when, in addition to the simulation, a limited amount of experimental data is collected from the real physical system. This article describes a method for selecting a conservative system design that is robust to uncertainty from the simulation parameters and simulation bias. The concept is that each potential system design is assigned a worst-case scenario in a data-driven feasible region. The conservative system design is then chosen as the best of the worst-cases. The method is shown to have good statistical properties. A case study is performed where a vehicle safety belt design is chosen to minimize the impact of vehicle crashes on a driver.

Original languageEnglish (US)
Pages (from-to)483-506
Number of pages24
JournalSIAM-ASA Journal on Uncertainty Quantification
Issue number2
StatePublished - Apr 1 2021


  • Applications to specific types of physical systems
  • Nonparametric inference
  • Simulation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics
  • Applied Mathematics


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