Inverse stochastic microstructure design.
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| Title: | Inverse stochastic microstructure design. |
|---|---|
| Authors: | Generale, Adam P.1 (AUTHOR), Robertson, Andreas E.1 (AUTHOR), Kelly, Conlain2 (AUTHOR), Kalidindi, Surya R.1,2 (AUTHOR) surya.kalidindi@me.gatech.edu |
| Source: | Acta Materialia. Jun2024, Vol. 271, pN.PAG-N.PAG. 1p. |
| Subjects: | Kriging, Statistical learning, Microstructure, Continuum mechanics, Statistical mechanics, Materials science, Ceramic-matrix composites |
| Abstract: | Inverse Microstructure Design problems are ubiquitous in materials science; for example, property-driven microstructure design requires the inversion of a structure–property linkage. However, prior frameworks have struggled to address this problem's unique combination of challenges: the high dimensionality and stochasticity of microstructures, under sampled initial datasets, and ill-conditioning of the inversion. In this work, we propose a computational framework for Inverse Microstructure Design problems using a Bayesian methodology. We construct this framework from three modular components, enabling flexible extension and re-use. First, we define a low-dimensional, informative microstructure prior by integrating domain knowledge (i.e., statistical continuum mechanics) into a distributional learning scheme. This scheme includes multiple latent representations which address the challenges inherent to representing microstructures. Second, we define a property-specific likelihood using a multi-output Gaussian process regression surrogate model. Finally, we efficiently learn the conditional posterior density for a given target property, and generate samples using deep variational inference. We demonstrate our proposed method for solving stochastic microstructure design problems by identifying woven ceramic matrix composites matching target anisotropic thermal conductivities. Through this example, we analyze the integral role of each component in the inversion framework. [Display omitted] [ABSTRACT FROM AUTHOR] |
| Copyright of Acta Materialia 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: 176647545 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Inverse stochastic microstructure design. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Generale%2C+Adam+P%2E%22">Generale, Adam P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Robertson%2C+Andreas+E%2E%22">Robertson, Andreas E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kelly%2C+Conlain%22">Kelly, Conlain</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kalidindi%2C+Surya+R%2E%22">Kalidindi, Surya R.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> surya.kalidindi@me.gatech.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Acta+Materialia%22">Acta Materialia</searchLink>. Jun2024, Vol. 271, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink><br /><searchLink fieldCode="DE" term="%22Microstructure%22">Microstructure</searchLink><br /><searchLink fieldCode="DE" term="%22Continuum+mechanics%22">Continuum mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+mechanics%22">Statistical mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+science%22">Materials science</searchLink><br /><searchLink fieldCode="DE" term="%22Ceramic-matrix+composites%22">Ceramic-matrix composites</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Inverse Microstructure Design problems are ubiquitous in materials science; for example, property-driven microstructure design requires the inversion of a structure–property linkage. However, prior frameworks have struggled to address this problem's unique combination of challenges: the high dimensionality and stochasticity of microstructures, under sampled initial datasets, and ill-conditioning of the inversion. In this work, we propose a computational framework for Inverse Microstructure Design problems using a Bayesian methodology. We construct this framework from three modular components, enabling flexible extension and re-use. First, we define a low-dimensional, informative microstructure prior by integrating domain knowledge (i.e., statistical continuum mechanics) into a distributional learning scheme. This scheme includes multiple latent representations which address the challenges inherent to representing microstructures. Second, we define a property-specific likelihood using a multi-output Gaussian process regression surrogate model. Finally, we efficiently learn the conditional posterior density for a given target property, and generate samples using deep variational inference. We demonstrate our proposed method for solving stochastic microstructure design problems by identifying woven ceramic matrix composites matching target anisotropic thermal conductivities. Through this example, we analyze the integral role of each component in the inversion framework. [Display omitted] [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Acta Materialia 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.actamat.2024.119877 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Kriging Type: general – SubjectFull: Statistical learning Type: general – SubjectFull: Microstructure Type: general – SubjectFull: Continuum mechanics Type: general – SubjectFull: Statistical mechanics Type: general – SubjectFull: Materials science Type: general – SubjectFull: Ceramic-matrix composites Type: general Titles: – TitleFull: Inverse stochastic microstructure design. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Generale, Adam P. – PersonEntity: Name: NameFull: Robertson, Andreas E. – PersonEntity: Name: NameFull: Kelly, Conlain – PersonEntity: Name: NameFull: Kalidindi, Surya R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13596454 Numbering: – Type: volume Value: 271 Titles: – TitleFull: Acta Materialia Type: main |
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