Hierarchical structured Stochastic Variational Inference for uncertainty quantification of viscoplastic constitutive models.
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| Title: | Hierarchical structured Stochastic Variational Inference for uncertainty quantification of viscoplastic constitutive models. |
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| Authors: | Chen, Tianju1,2 (AUTHOR) chentianju@usst.edu.cn, Messner, Mark C.1,2 (AUTHOR) messner@anl.gov, Fu, Huadong1,3 (AUTHOR) hdfu@ustb.edu.cn |
| Source: | European Journal of Mechanics A: Solids. Sep2026, Vol. 119, pN.PAG-N.PAG. 1p. |
| Subjects: | Hierarchical Bayes model, Bayesian analysis, Uncertainty (Information theory), Mechanical behavior of materials, Calibration |
| Abstract: | We present a Bayesian approach to constitutive modeling that leverages Stochastic Variational Inference (SVI) for efficient uncertainty quantification of viscoplastic material response. Traditional methods struggle to characterize variability from material heterogeneity and experimental noise with limited datasets. Our SVI implementation in pyoptmat overcomes this by: (1) Using hierarchical priors to share statistical strength across temperatures, reducing data requirements compared to non-hierarchical methods; (2) Enabling rapid posterior estimation (much faster than Markov Chain Monte Carlo (MCMC)) through stochastic gradient optimization; and (3) Preserving full uncertainty quantification via variational distributions. Validated against synthetic data and applied to Alloy 800H at multiple temperatures, the approach produces temperature-dependent statistical models that accurately capture stress–strain uncertainty. The hierarchical SVI framework is especially adept at handling sparse experimental data, effectively distinguishing between true material variability and experimental noise where traditional calibration techniques cannot. Our approach bypasses the computational limitations that have traditionally hindered full uncertainty propagation in complex constitutive models. This capability leads to substantially improved model calibration and robust extrapolation, enhancing the model's utility in engineering design and analysis. • Hierarchical priors share info across temps, reducing data needs vs standard methods • SVI is much faster than MCMC, enabling rapid model calibration • pyoptmat gives full UQ via var. distributions, typically hard for complex models • Validated on synthetic & real data for Alloy 800H, giving accurate T-dependent models • Leads to better calibration & robust extrapolation, improving design & safety utility [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Mechanics A: Solids is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194296532 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hierarchical structured Stochastic Variational Inference for uncertainty quantification of viscoplastic constitutive models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Tianju%22">Chen, Tianju</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chentianju@usst.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Messner%2C+Mark+C%2E%22">Messner, Mark C.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> messner@anl.gov</i><br /><searchLink fieldCode="AR" term="%22Fu%2C+Huadong%22">Fu, Huadong</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> hdfu@ustb.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Mechanics+A%3A+Solids%22">European Journal of Mechanics A: Solids</searchLink>. Sep2026, Vol. 119, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hierarchical+Bayes+model%22">Hierarchical Bayes model</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+behavior+of+materials%22">Mechanical behavior of materials</searchLink><br /><searchLink fieldCode="DE" term="%22Calibration%22">Calibration</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present a Bayesian approach to constitutive modeling that leverages Stochastic Variational Inference (SVI) for efficient uncertainty quantification of viscoplastic material response. Traditional methods struggle to characterize variability from material heterogeneity and experimental noise with limited datasets. Our SVI implementation in pyoptmat overcomes this by: (1) Using hierarchical priors to share statistical strength across temperatures, reducing data requirements compared to non-hierarchical methods; (2) Enabling rapid posterior estimation (much faster than Markov Chain Monte Carlo (MCMC)) through stochastic gradient optimization; and (3) Preserving full uncertainty quantification via variational distributions. Validated against synthetic data and applied to Alloy 800H at multiple temperatures, the approach produces temperature-dependent statistical models that accurately capture stress–strain uncertainty. The hierarchical SVI framework is especially adept at handling sparse experimental data, effectively distinguishing between true material variability and experimental noise where traditional calibration techniques cannot. Our approach bypasses the computational limitations that have traditionally hindered full uncertainty propagation in complex constitutive models. This capability leads to substantially improved model calibration and robust extrapolation, enhancing the model's utility in engineering design and analysis. • Hierarchical priors share info across temps, reducing data needs vs standard methods • SVI is much faster than MCMC, enabling rapid model calibration • pyoptmat gives full UQ via var. distributions, typically hard for complex models • Validated on synthetic & real data for Alloy 800H, giving accurate T-dependent models • Leads to better calibration & robust extrapolation, improving design & safety utility [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Mechanics A: Solids is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.euromechsol.2026.106139 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Hierarchical Bayes model Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Uncertainty (Information theory) Type: general – SubjectFull: Mechanical behavior of materials Type: general – SubjectFull: Calibration Type: general Titles: – TitleFull: Hierarchical structured Stochastic Variational Inference for uncertainty quantification of viscoplastic constitutive models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Tianju – PersonEntity: Name: NameFull: Messner, Mark C. – PersonEntity: Name: NameFull: Fu, Huadong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 09977538 Numbering: – Type: volume Value: 119 Titles: – TitleFull: European Journal of Mechanics A: Solids Type: main |
| ResultId | 1 |
