Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures.
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| Title: | Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures. |
|---|---|
| Authors: | Messner, Mark C.1 messner@anl.gov |
| Source: | Journal of Mechanical Design. Feb2020, Vol. 142 Issue 2, p1-6. 6p. |
| Subjects: | Artificial neural networks, Mechanical models, Inverse problems |
| Abstract: | This work describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers 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: 140910637 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Messner%2C+Mark+C%2E%22">Messner, Mark C.</searchLink><relatesTo>1</relatesTo><i> messner@anl.gov</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Design%22">Journal of Mechanical Design</searchLink>. Feb2020, Vol. 142 Issue 2, p1-6. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+models%22">Mechanical models</searchLink><br /><searchLink fieldCode="DE" term="%22Inverse+problems%22">Inverse problems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This work describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers 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: 10.1115/1.4045040 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Mechanical models Type: general – SubjectFull: Inverse problems Type: general Titles: – TitleFull: Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Messner, Mark C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 10500472 Numbering: – Type: volume Value: 142 – Type: issue Value: 2 Titles: – TitleFull: Journal of Mechanical Design Type: main |
| ResultId | 1 |
