Academic Journal
Neural Level Set Topology Optimization Using Unfitted Finite Elements.
| Τίτλος: | Neural Level Set Topology Optimization Using Unfitted Finite Elements. |
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| Συγγραφείς: | Mallon, Connor N., Thornton, Aaron W., Hill, Matthew R., Badia, Santiago |
| Πηγή: | International Journal for Numerical Methods in Engineering; 3/30/2025, Vol. 126 Issue 6, p1-17, 17p |
| Θεματικοί όροι: | Finite element method, Level set methods, Engineering design, Artificial neural networks, Mathematical optimization, Automatic differentiation, Structural optimization |
| Περίληψη: | To facilitate the widespread adoption of automated engineering design techniques, existing methods must become more efficient and generalizable. In the field of topology optimization, this requires the coupling of modern optimization methods with solvers capable of handling arbitrary problems. In this work, a topology optimization method for general multiphysics problems is presented. We leverage a convolutional neural parameterization of a level set for a description of the geometry and use this in an unfitted finite element method that is differentiable with respect to the level set everywhere in the domain. We construct the parameter to objective map in such a way that the gradient can be computed entirely by automatic differentiation at roughly the cost of an objective function evaluation. Without handcrafted initializations, the method produces regular topologies close to the optimal solution for standard benchmark problems whilst maintaining the ability to solve a more general class of problems than standard methods, for example, interface‐coupled multiphysics. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal for Numerical Methods in Engineering is the property of Wiley-Blackwell 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 184043685 RelevancyScore: 994 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 993.543701171875 |
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| Items | – Name: Title Label: Title Group: Ti Data: Neural Level Set Topology Optimization Using Unfitted Finite Elements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mallon%2C+Connor+N%2E%22">Mallon, Connor N.</searchLink><br /><searchLink fieldCode="AR" term="%22Thornton%2C+Aaron+W%2E%22">Thornton, Aaron W.</searchLink><br /><searchLink fieldCode="AR" term="%22Hill%2C+Matthew+R%2E%22">Hill, Matthew R.</searchLink><br /><searchLink fieldCode="AR" term="%22Badia%2C+Santiago%22">Badia, Santiago</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal for Numerical Methods in Engineering; 3/30/2025, Vol. 126 Issue 6, p1-17, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Level+set+methods%22">Level set methods</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To facilitate the widespread adoption of automated engineering design techniques, existing methods must become more efficient and generalizable. In the field of topology optimization, this requires the coupling of modern optimization methods with solvers capable of handling arbitrary problems. In this work, a topology optimization method for general multiphysics problems is presented. We leverage a convolutional neural parameterization of a level set for a description of the geometry and use this in an unfitted finite element method that is differentiable with respect to the level set everywhere in the domain. We construct the parameter to objective map in such a way that the gradient can be computed entirely by automatic differentiation at roughly the cost of an objective function evaluation. Without handcrafted initializations, the method produces regular topologies close to the optimal solution for standard benchmark problems whilst maintaining the ability to solve a more general class of problems than standard methods, for example, interface‐coupled multiphysics. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal for Numerical Methods in Engineering is the property of Wiley-Blackwell 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: 10.1002/nme.70004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Finite element method Type: general – SubjectFull: Level set methods Type: general – SubjectFull: Engineering design Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Automatic differentiation Type: general – SubjectFull: Structural optimization Type: general Titles: – TitleFull: Neural Level Set Topology Optimization Using Unfitted Finite Elements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mallon, Connor N. – PersonEntity: Name: NameFull: Thornton, Aaron W. – PersonEntity: Name: NameFull: Hill, Matthew R. – PersonEntity: Name: NameFull: Badia, Santiago IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 03 Text: 3/30/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00295981 Numbering: – Type: volume Value: 126 – Type: issue Value: 6 Titles: – TitleFull: International Journal for Numerical Methods in Engineering Type: main |
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