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International Journal for Uncertainty Quantification

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ISSN Druckformat: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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

Volumen 12, Ausgabe 6, 2022, pp. 47-69
DOI: 10.1615/Int.J.UncertaintyQuantification.2022039747
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ABSTRAKT

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

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