EVALUATION OF MODEL BIAS IDENTIFICATION APPROACHES BASED ON BAYESIAN INFERENCE AND APPLICATIONS TO DIGITAL TWINS Article Swipe
Daniel Andrés Arcones
,
Martin Weiser
,
Phaedon‐Stelios Koutsourelakis
,
Jörg F. Unger
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7712/120223.10325.19795
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.7712/120223.10325.19795
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded.Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications.Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built.One of the main contributors to this difference in addition to miscalibrated model parameters is the model error.Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins.Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model.Bayesian formulations can tackle this task.Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model.Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model.This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods.This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes.Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.67
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7712/120223.10325.19795
- https://files.eccomasproceedia.org/papers/uncecomp-2023/U23_19795.pdf?mtime=20231027200347
- OA Status
- bronze
- Cited By
- 2
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387986281
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Raw OpenAlex JSON
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https://openalex.org/W4387986281Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7712/120223.10325.19795Digital Object Identifier
- Title
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EVALUATION OF MODEL BIAS IDENTIFICATION APPROACHES BASED ON BAYESIAN INFERENCE AND APPLICATIONS TO DIGITAL TWINSWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Daniel Andrés Arcones, Martin Weiser, Phaedon‐Stelios Koutsourelakis, Jörg F. UngerList of authors in order
- Landing page
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https://doi.org/10.7712/120223.10325.19795Publisher landing page
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https://files.eccomasproceedia.org/papers/uncecomp-2023/U23_19795.pdf?mtime=20231027200347Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://files.eccomasproceedia.org/papers/uncecomp-2023/U23_19795.pdf?mtime=20231027200347Direct OA link when available
- Concepts
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Computer science, Inference, Identification (biology), Bayesian probability, Bayesian inference, Artificial intelligence, Machine learning, Data mining, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 2Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.probability | 139 |
| abstract_inverted_index.twins.Model | 96 |
| abstract_inverted_index.uncertainty | 144 |
| abstract_inverted_index.contributors | 61 |
| abstract_inverted_index.distribution | 140 |
| abstract_inverted_index.formulations | 115 |
| abstract_inverted_index.measurements | 52, 107, 147 |
| abstract_inverted_index.methods.This | 186 |
| abstract_inverted_index.capabilities, | 221 |
| abstract_inverted_index.computational | 223 |
| abstract_inverted_index.effectiveness | 175 |
| abstract_inverted_index.miscalibrated | 68 |
| abstract_inverted_index.expanded.Their | 19 |
| abstract_inverted_index.generalization | 220 |
| abstract_inverted_index.identification | 98, 180 |
| abstract_inverted_index.model.Bayesian | 114 |
| abstract_inverted_index.task.Including | 119 |
| abstract_inverted_index.Processes.Their | 214 |
| abstract_inverted_index.simulation-based | 6 |
| abstract_inverted_index.error.Quantifying | 74 |
| abstract_inverted_index.model.Simultaneously, | 150 |
| abstract_inverted_index.applications.Nonetheless, | 40 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |