Multifidelity Uncertainty Quantification for Ice Sheet Simulations Article Swipe
Nicole Aretz
,
Max Gunzburger
,
Mathieu Morlighem
,
Karen Willcox
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.06110
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.06110
Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations. UQ methods typically employ Monte Carlo simulation to estimate statistics of interest, which requires hundreds (or more) of ice sheet simulations. Cheaper low-fidelity models are readily available (e.g., approximated physics, coarser meshes), but replacing the high-fidelity model with a lower fidelity surrogate introduces bias, which means that UQ results generated with a low-fidelity model cannot be rigorously trusted. Multifidelity UQ retains the high-fidelity model but expands the estimator to shift computations to low-fidelity models, while still guaranteeing an unbiased estimate. Through this exploitation of multiple models, multifidelity estimators guarantee a target accuracy at reduced computational cost. This paper presents a comprehensive multifidelity UQ framework for ice sheet simulations. We present three multifidelity UQ approaches -- Multifidelity Monte Carlo, Multilevel Monte Carlo, and the Best Linear Unbiased Estimator -- that enable tractable UQ for continental-scale ice sheet simulations. We demonstrate the techniques on a model of the Greenland ice sheet to estimate the 2015-2050 ice mass loss, verify their estimates through comparison with Monte Carlo simulations, and give a comparative performance analysis. For a target accuracy equivalent to 1 mm sea level rise contribution at 95% confidence, the multifidelity estimators achieve computational speedups of two orders of magnitude.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.06110
- https://arxiv.org/pdf/2412.06110
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405253915
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4405253915Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.06110Digital Object Identifier
- Title
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Multifidelity Uncertainty Quantification for Ice Sheet SimulationsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-12-08Full publication date if available
- Authors
-
Nicole Aretz, Max Gunzburger, Mathieu Morlighem, Karen WillcoxList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.06110Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.06110Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.06110Direct OA link when available
- Concepts
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Ice sheet, Uncertainty quantification, Environmental science, Climatology, Geology, Geomorphology, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| institutions_distinct_count | 4 |
| citation_normalized_percentile |