Calibration Using Emulation of Filtered Simulation Results

Ozge Surer, Matthew Plumlee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Calibration of parameters in simulation models is necessary to develop sharp predictions with quantified uncertainty. A scalable method for calibration involves building an emulator after conducting an experiment on the simulation model. However, when the parameter space is large, meaning the parameters are quite uncertain prior to calibration, much of the parameter space can produce unstable or unrealistic simulator responses that drastically differ from the observed data. One solution to this problem is to simply discard, or filter out, the parameters that gave unreasonable responses and then build an emulator only on the remaining simulator responses. In this article, we demonstrate the key mechanics for an approach that emulates filtered responses but also avoids unstable and incorrect inference. These ideas are illustrated on a real data example of calibrating COVID-19 epidemiological simulation model.

Original languageEnglish (US)
Title of host publication2021 Winter Simulation Conference, WSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433112
StatePublished - 2021
Event2021 Winter Simulation Conference, WSC 2021 - Phoenix, United States
Duration: Dec 12 2021Dec 15 2021

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2021 Winter Simulation Conference, WSC 2021
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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