Koermer, ScottLoda, JustinNoble, AaronGramacy, Robert B.2025-02-182025-02-182024-05-240040-1706https://hdl.handle.net/10919/124617The 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.Pages 638-65013 page(s)application/pdfenIn CopyrightGaussian processesIntegrated mean squared errorInverse problemSequential designArticle - Refereedhttps://doi.org/10.1080/00401706.2024.2345139664Noble, Christopher [0000-0002-8860-9472]Gramacy, Robert [0000-0001-9308-3615]Loda, Justin [0000-0002-0072-1735]1537-2723