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

Abstract

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
JournalCiencia Tecnologia Agropecuaria
Volume22
Issue number1
DOIs
StatePublished - Apr 1 2021

Keywords

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

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)

Fingerprint

Dive into the research topics of 'Robust system design with limited experimental data and an inexact simulation model'. Together they form a unique fingerprint.

Cite this