Academic Journal
DEVELOPING OF NEURAL NETWORK COMPUTING METHODS FOR SOLVING INVERSE ELASTICITY PROBLEMS.
| Τίτλος: | DEVELOPING OF NEURAL NETWORK COMPUTING METHODS FOR SOLVING INVERSE ELASTICITY PROBLEMS. |
|---|---|
| Alternate Title: | РОЗРОБКА НЕЙРОМЕРЕЖЕВИХ ОБЧИСЛЮВАЛЬНИХ МЕТОДІВ ДЛЯ РОЗВ’ЯЗАННЯ ОБЕРНЕНИХ ЗАДАЧ ПРУЖНОСТІ. (Ukrainian) |
| Συγγραφείς: | Kaliuzhniak, Anastasiia, Kudin, Oleksii, Belokon, Yuriy, Kruglyak, Dmytro |
| Πηγή: | Eastern-European Journal of Enterprise Technologies; 2024, Vol. 132 Issue 7, p45-52, 8p |
| Θεματικοί όροι: | Symbolic computation, Boundary value problems, Artificial neural networks, Inverse problems, Software libraries (Computer programming) |
| Περίληψη: | This paper examines the use of neural network methods to solve inverse problems in the mechanics of elastic materials. The aim is to design physics-informed neural networks that can predict the parameters of structural components, and the physical properties of materials based on a specified displacement distribution. A key feature of the specified neural networks is the integration of differential equations and boundary conditions into the loss function calculation. This approach ensures that the error in approximating unknown functions has a direct impact on optimizing the network’s weights. As a result, the resulting neural network approximations of unknown functions comply with the differential equations and boundary conditions. To test the capabilities of the designed neural networks, inverse problems involving the bending of plates and beams have been solved, focusing on determining one or two unknown parameters. Comparison of predicted and exact values demonstrates high accuracy of the constructed neural network models, with a relative prediction error of less than 3 % across all cases. Unlike analytical methods for solving inverse problems, the primary advantage of physics-informed neural networks is their flexibility when addressing both linear and nonlinear problems. For instance, the same network architecture can be employed to solve various boundary-value problems without modification. Compared to classical numerical methods, the parallelization capability of neural networks is inherently supported by modern software libraries. Therefore, the application of physics-informed neural networks for solving inverse elasticity problems of plates and beams is effective, as evidenced by the achieved relative errors and the computational robustness of the method. In practice, the proposed solution can be used for relevant calculations during the design of structural elements. The developed software code can also be integrated into automated design systems or computer algebra systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Eastern-European Journal of Enterprise Technologies is the property of PC TECHNOLOGY CENTER 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 | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=17293774&ISBN=&volume=132&issue=7&date=20240829&spage=45&pages=45-52&title=Eastern-European Journal of Enterprise Technologies&atitle=DEVELOPING%20OF%20NEURAL%20NETWORK%20COMPUTING%20METHODS%20FOR%20SOLVING%20INVERSE%20ELASTICITY%20PROBLEMS.&aulast=Kaliuzhniak%2C%20Anastasiia&id=DOI:10.15587/1729-4061.2024.313795 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: DEVELOPING OF NEURAL NETWORK COMPUTING METHODS FOR SOLVING INVERSE ELASTICITY PROBLEMS. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: РОЗРОБКА НЕЙРОМЕРЕЖЕВИХ ОБЧИСЛЮВАЛЬНИХ МЕТОДІВ ДЛЯ РОЗВ’ЯЗАННЯ ОБЕРНЕНИХ ЗАДАЧ ПРУЖНОСТІ. (Ukrainian) – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kaliuzhniak%2C+Anastasiia%22">Kaliuzhniak, Anastasiia</searchLink><br /><searchLink fieldCode="AR" term="%22Kudin%2C+Oleksii%22">Kudin, Oleksii</searchLink><br /><searchLink fieldCode="AR" term="%22Belokon%2C+Yuriy%22">Belokon, Yuriy</searchLink><br /><searchLink fieldCode="AR" term="%22Kruglyak%2C+Dmytro%22">Kruglyak, Dmytro</searchLink> – Name: TitleSource Label: Source Group: Src Data: Eastern-European Journal of Enterprise Technologies; 2024, Vol. 132 Issue 7, p45-52, 8p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Symbolic+computation%22">Symbolic computation</searchLink><br /><searchLink fieldCode="DE" term="%22Boundary+value+problems%22">Boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Inverse+problems%22">Inverse problems</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper examines the use of neural network methods to solve inverse problems in the mechanics of elastic materials. The aim is to design physics-informed neural networks that can predict the parameters of structural components, and the physical properties of materials based on a specified displacement distribution. A key feature of the specified neural networks is the integration of differential equations and boundary conditions into the loss function calculation. This approach ensures that the error in approximating unknown functions has a direct impact on optimizing the network’s weights. As a result, the resulting neural network approximations of unknown functions comply with the differential equations and boundary conditions. To test the capabilities of the designed neural networks, inverse problems involving the bending of plates and beams have been solved, focusing on determining one or two unknown parameters. Comparison of predicted and exact values demonstrates high accuracy of the constructed neural network models, with a relative prediction error of less than 3 % across all cases. Unlike analytical methods for solving inverse problems, the primary advantage of physics-informed neural networks is their flexibility when addressing both linear and nonlinear problems. For instance, the same network architecture can be employed to solve various boundary-value problems without modification. Compared to classical numerical methods, the parallelization capability of neural networks is inherently supported by modern software libraries. Therefore, the application of physics-informed neural networks for solving inverse elasticity problems of plates and beams is effective, as evidenced by the achieved relative errors and the computational robustness of the method. In practice, the proposed solution can be used for relevant calculations during the design of structural elements. The developed software code can also be integrated into automated design systems or computer algebra systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Eastern-European Journal of Enterprise Technologies is the property of PC TECHNOLOGY CENTER 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: 10.15587/1729-4061.2024.313795 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 45 Subjects: – SubjectFull: Symbolic computation Type: general – SubjectFull: Boundary value problems Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Inverse problems Type: general – SubjectFull: Software libraries (Computer programming) Type: general Titles: – TitleFull: DEVELOPING OF NEURAL NETWORK COMPUTING METHODS FOR SOLVING INVERSE ELASTICITY PROBLEMS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kaliuzhniak, Anastasiia – PersonEntity: Name: NameFull: Kudin, Oleksii – PersonEntity: Name: NameFull: Belokon, Yuriy – PersonEntity: Name: NameFull: Kruglyak, Dmytro IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 08 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17293774 Numbering: – Type: volume Value: 132 – Type: issue Value: 7 Titles: – TitleFull: Eastern-European Journal of Enterprise Technologies Type: main |
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