EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS

نویسندگان

چکیده

Calibration of expensive computer models using emulators for high-dimensional output fields can become increasingly intractable with the size field(s) being compared to observational data. In these settings, dimension reduction is attractive, reducing number required mimic by orders magnitude. By comparing popular independent emulation approaches that fit univariate each grid cell in field, we demonstrate a basis structure emulation, aside from clear computational benefits, essential obtaining coherent draws be data or used prediction. We show calibrating on subspace spanned not generally equivalent full field (the latter infeasible owing large matrix inversions calibration and matrices field). then present projection allows accurate exactly cost subspace, projecting norm induced our uncertainties observations model discrepancy given one-off inversion matrix. illustrate benefits approach compare standard emulating high dimensional ice sheet Glimmer.

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ژورنال

عنوان ژورنال: International Journal for Uncertainty Quantification

سال: 2022

ISSN: ['2152-5080', '2152-5099']

DOI: https://doi.org/10.1615/int.j.uncertaintyquantification.2022039747