Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration Article Swipe
Daniel Andrés Arcones
,
Martin Weiser
,
Phaedon‐Stelios Koutsourelakis
,
Jörg F. Unger
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.12037
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.12037
A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction's reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature observations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.12037
- https://arxiv.org/pdf/2410.12037
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403577294
All OpenAlex metadata
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https://openalex.org/W4403577294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.12037Digital Object Identifier
- Title
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Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model CalibrationWork 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
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2024-10-15Full publication date if available
- Authors
-
Daniel Andrés Arcones, Martin Weiser, Phaedon‐Stelios Koutsourelakis, Jörg F. UngerList of authors in order
- Landing page
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https://arxiv.org/abs/2410.12037Publisher landing page
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https://arxiv.org/pdf/2410.12037Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.12037Direct OA link when available
- Concepts
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Calibration, Bayesian probability, Noise (video), Computer science, Statistics, Artificial intelligence, Mathematics, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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