Academic Journal
Data-driven model order reduction with surrogate elements for transient simulations.
| Τίτλος: | Data-driven model order reduction with surrogate elements for transient simulations. |
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| Συγγραφείς: | Franke, Markus, Krause, Tom Janis, Wagner, Marcus |
| Πηγή: | Engineering Computations; 2026, Vol. 43 Issue 7, p2655-2680, 26p |
| Θεματικοί όροι: | Finite element method, Degrees of freedom, Mathematical optimization, Artificial neural networks, Matrices (Mathematics), Reduced-order models, Dynamic simulation |
| Περίληψη: | Purpose: The purpose of this study is to introduce surrogate elements for static and transient finite element simulations. These elements are designed to replace regions of several conventional solid elements with a single artificial element that possesses a reduced number of degrees of freedoms (dofs). A notable advantage of our surrogate elements is their seamless integration into standard finite element meshes. Design/methodology/approach: The construction of the surrogate elements stiffness and mass matrices is achieved through an optimization process wherein displacements serve as the optimization objective. Moreover, the matrices are designed to possess properties analogous to those of standard finite elements. A particular focus is placed on ensuring that the artificial stiffness matrices are positive semi-definite. Furthermore, artificial degrees of freedom are introduced. Findings: The efficacy of the proposed technique is demonstrated through its application to two different use cases. It is demonstrated that, despite being trained on examples comprising a single surrogate element, the surrogate elements can be employed multiple times within complex and practical models. The degree of accuracy achieved in these applications is noteworthy. Moreover, the proposed method is considerably faster than the fully discretized models. Originality/value: The study expands the field of substructuring and model order reduction by incorporating artificial surrogate elements built by neural networks, which enables seamless integration with standard finite element analysis via positive semi-definite matrices. Furthermore, the introduction of artificial degrees of freedom, which are detached from the computational domain, is proposed. Once trained, the surrogate elements can be utilised in load and support independent scenarios. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 195151369 RelevancyScore: 1067 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1066.67932128906 |
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| Items | – Name: Title Label: Title Group: Ti Data: Data-driven model order reduction with surrogate elements for transient simulations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Franke%2C+Markus%22">Franke, Markus</searchLink><br /><searchLink fieldCode="AR" term="%22Krause%2C+Tom+Janis%22">Krause, Tom Janis</searchLink><br /><searchLink fieldCode="AR" term="%22Wagner%2C+Marcus%22">Wagner, Marcus</searchLink> – Name: TitleSource Label: Source Group: Src Data: Engineering Computations; 2026, Vol. 43 Issue 7, p2655-2680, 26p – 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="%22Degrees+of+freedom%22">Degrees of freedom</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Matrices+%28Mathematics%29%22">Matrices (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Reduced-order+models%22">Reduced-order models</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+simulation%22">Dynamic simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: The purpose of this study is to introduce surrogate elements for static and transient finite element simulations. These elements are designed to replace regions of several conventional solid elements with a single artificial element that possesses a reduced number of degrees of freedoms (dofs). A notable advantage of our surrogate elements is their seamless integration into standard finite element meshes. Design/methodology/approach: The construction of the surrogate elements stiffness and mass matrices is achieved through an optimization process wherein displacements serve as the optimization objective. Moreover, the matrices are designed to possess properties analogous to those of standard finite elements. A particular focus is placed on ensuring that the artificial stiffness matrices are positive semi-definite. Furthermore, artificial degrees of freedom are introduced. Findings: The efficacy of the proposed technique is demonstrated through its application to two different use cases. It is demonstrated that, despite being trained on examples comprising a single surrogate element, the surrogate elements can be employed multiple times within complex and practical models. The degree of accuracy achieved in these applications is noteworthy. Moreover, the proposed method is considerably faster than the fully discretized models. Originality/value: The study expands the field of substructuring and model order reduction by incorporating artificial surrogate elements built by neural networks, which enables seamless integration with standard finite element analysis via positive semi-definite matrices. Furthermore, the introduction of artificial degrees of freedom, which are detached from the computational domain, is proposed. Once trained, the surrogate elements can be utilised in load and support independent scenarios. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Engineering Computations is the property of Emerald Publishing Limited 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2655 Subjects: – SubjectFull: Finite element method Type: general – SubjectFull: Degrees of freedom Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Matrices (Mathematics) Type: general – SubjectFull: Reduced-order models Type: general – SubjectFull: Dynamic simulation Type: general Titles: – TitleFull: Data-driven model order reduction with surrogate elements for transient simulations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Franke, Markus – PersonEntity: Name: NameFull: Krause, Tom Janis – PersonEntity: Name: NameFull: Wagner, Marcus IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02644401 Numbering: – Type: volume Value: 43 – Type: issue Value: 7 Titles: – TitleFull: Engineering Computations Type: main |
| ResultId | 1 |