Multi-output calibration of a honeycomb seal via on-site surrogates
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We consider large-scale industrial computer model calibration, combining multi-output simulation with limited physical observation, involved in the development of a honeycomb seal. Toward that end, we adopt a localized sampling and emulation strategy called "on-site surrogates (OSSs)", designed to cope with the amalgamated challenges of high-dimensional inputs, large-scale simulation campaigns, and nonstationary response surfaces. In previous applications, OSSs were one-at-a-time affairs for multiple outputs. We demonstrate that this leads to dissonance in calibration efforts for a common parameter set across outputs for the honeycomb. Instead, a conceptually straightforward, but implementationally intricate, principal-components representation, adapted from ordinary Gaussian process surrogate modeling to the OSS setting, can resolve this tension. With a two-pronged - optimization-based and fully Bayesian - approach, we show how pooled information across outputs can reduce uncertainty and enhance (statistical and computational) efficiency in calibrated parameters for the honeycomb relative to the previous, "data-poor" univariate analog.
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