Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

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dc.contributor.authorKoermer, Scotten
dc.contributor.authorLoda, Justinen
dc.contributor.authorNoble, Aaronen
dc.contributor.authorGramacy, Robert B.en
dc.date.accessioned2025-02-18T13:08:18Zen
dc.date.available2025-02-18T13:08:18Zen
dc.date.issued2024-05-24en
dc.description.abstractThe Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.en
dc.description.versionAccepted versionen
dc.format.extentPages 638-650en
dc.format.extent13 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/00401706.2024.2345139en
dc.identifier.eissn1537-2723en
dc.identifier.issn0040-1706en
dc.identifier.issue4en
dc.identifier.orcidNoble, Christopher [0000-0002-8860-9472]en
dc.identifier.orcidGramacy, Robert [0000-0001-9308-3615]en
dc.identifier.orcidLoda, Justin [0000-0002-0072-1735]en
dc.identifier.urihttps://hdl.handle.net/10919/124617en
dc.identifier.volume66en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectGaussian processesen
dc.subjectIntegrated mean squared erroren
dc.subjectInverse problemen
dc.subjectSequential designen
dc.titleAugmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experimentsen
dc.title.serialTechnometricsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Scienceen
pubs.organisational-groupVirginia Tech/Science/Statisticsen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Mining and Minerals Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-groupVirginia Tech/Science/COS T&R Facultyen

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