Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy Article Swipe
Isabela Coelho Lima
,
Annika Robens‐Radermacher
,
Thomas Titscher
,
Daniel Kadoke
,
Phaedon‐Stelios Koutsourelakis
,
Jörg F. Unger
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00466-022-02214-6
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s00466-022-02214-6
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future of monitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.
Related Topics
Concepts
Surrogate model
Uncertainty quantification
Computer science
Algorithm
Bayesian probability
Divergence (linguistics)
Identification (biology)
Bayesian inference
Field (mathematics)
Mathematical optimization
Bayes' theorem
Convergence (economics)
Inference
Machine learning
Mathematics
Artificial intelligence
Pure mathematics
Economics
Botany
Biology
Economic growth
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00466-022-02214-6
- https://link.springer.com/content/pdf/10.1007/s00466-022-02214-6.pdf
- OA Status
- hybrid
- Cited By
- 4
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292478225
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4292478225Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s00466-022-02214-6Digital Object Identifier
- Title
-
Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-18Full publication date if available
- Authors
-
Isabela Coelho Lima, Annika Robens‐Radermacher, Thomas Titscher, Daniel Kadoke, Phaedon‐Stelios Koutsourelakis, Jörg F. UngerList of authors in order
- Landing page
-
https://doi.org/10.1007/s00466-022-02214-6Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s00466-022-02214-6.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s00466-022-02214-6.pdfDirect OA link when available
- Concepts
-
Surrogate model, Uncertainty quantification, Computer science, Algorithm, Bayesian probability, Divergence (linguistics), Identification (biology), Bayesian inference, Field (mathematics), Mathematical optimization, Bayes' theorem, Convergence (economics), Inference, Machine learning, Mathematics, Artificial intelligence, Pure mathematics, Economics, Botany, Biology, Economic growth, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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