Academic Journal
Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem.
| Τίτλος: | Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. |
|---|---|
| Συγγραφείς: | Singh, Vishal, Harursampath, Dineshkumar, Dhawan, Sharanjeet, Sahni, Manoj, Saxena, Sahaj, Mallick, Rajnish |
| Πηγή: | Modelling; Dec2024, Vol. 5 Issue 4, p1532-1549, 18p |
| Θεματικοί όροι: | Artificial neural networks, Boundary value problems, Partial differential equations, Solid mechanics, Differential equations, Deep learning |
| Περίληψη: | Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade. The blade is regarded as a one-dimensional prismatic cantilever beam that is exposed to triangular loading, and comprehending its mechanical behavior is of utmost importance in the aerospace field. PINNs utilize the physical information, including differential equations and boundary conditions, within the loss function of the neural network to approximate the solution. Our approach determines the overall loss by aggregating the losses from the differential equation, boundary conditions, and data. We employed a physics-informed neural network (PINN) and an artificial neural network (ANN) with equivalent hyperparameters to solve a fourth-order differential equation. By comparing the performance of the PINN model against the analytical solution of the equation and the results obtained from the ANN model, we have conclusively shown that the PINN model exhibits superior accuracy, robustness, and computational efficiency when addressing high-order differential equations that govern physics-based problems. In conclusion, the study demonstrates that PINN offers a superior alternative for addressing solid mechanics problems with applications in the aerospace industry. [ABSTRACT FROM AUTHOR] |
| Copyright of Modelling is the property of MDPI 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 |
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| Header | DbId: edb DbLabel: Complementary Index An: 181946640 RelevancyScore: 983 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 983.441223144531 |
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| Items | – Name: Title Label: Title Group: Ti Data: Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Vishal%22">Singh, Vishal</searchLink><br /><searchLink fieldCode="AR" term="%22Harursampath%2C+Dineshkumar%22">Harursampath, Dineshkumar</searchLink><br /><searchLink fieldCode="AR" term="%22Dhawan%2C+Sharanjeet%22">Dhawan, Sharanjeet</searchLink><br /><searchLink fieldCode="AR" term="%22Sahni%2C+Manoj%22">Sahni, Manoj</searchLink><br /><searchLink fieldCode="AR" term="%22Saxena%2C+Sahaj%22">Saxena, Sahaj</searchLink><br /><searchLink fieldCode="AR" term="%22Mallick%2C+Rajnish%22">Mallick, Rajnish</searchLink> – Name: TitleSource Label: Source Group: Src Data: Modelling; Dec2024, Vol. 5 Issue 4, p1532-1549, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Boundary+value+problems%22">Boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Partial+differential+equations%22">Partial differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Solid+mechanics%22">Solid mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+equations%22">Differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade. The blade is regarded as a one-dimensional prismatic cantilever beam that is exposed to triangular loading, and comprehending its mechanical behavior is of utmost importance in the aerospace field. PINNs utilize the physical information, including differential equations and boundary conditions, within the loss function of the neural network to approximate the solution. Our approach determines the overall loss by aggregating the losses from the differential equation, boundary conditions, and data. We employed a physics-informed neural network (PINN) and an artificial neural network (ANN) with equivalent hyperparameters to solve a fourth-order differential equation. By comparing the performance of the PINN model against the analytical solution of the equation and the results obtained from the ANN model, we have conclusively shown that the PINN model exhibits superior accuracy, robustness, and computational efficiency when addressing high-order differential equations that govern physics-based problems. In conclusion, the study demonstrates that PINN offers a superior alternative for addressing solid mechanics problems with applications in the aerospace industry. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Modelling is the property of MDPI 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: 10.3390/modelling5040080 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1532 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Boundary value problems Type: general – SubjectFull: Partial differential equations Type: general – SubjectFull: Solid mechanics Type: general – SubjectFull: Differential equations Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Vishal – PersonEntity: Name: NameFull: Harursampath, Dineshkumar – PersonEntity: Name: NameFull: Dhawan, Sharanjeet – PersonEntity: Name: NameFull: Sahni, Manoj – PersonEntity: Name: NameFull: Saxena, Sahaj – PersonEntity: Name: NameFull: Mallick, Rajnish IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 26733951 Numbering: – Type: volume Value: 5 – Type: issue Value: 4 Titles: – TitleFull: Modelling Type: main |
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