Treatment of non-physical solutions of the oxygen diffusion in soil by physics-informed neural network.
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| Title: | Treatment of non-physical solutions of the oxygen diffusion in soil by physics-informed neural network. |
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| Authors: | Ivanović, Miloš1 (AUTHOR) mivanovic@kg.ac.rs, Savović, Svetislav1 (AUTHOR) savovic@kg.ac.rs, Kuzmanović, Ljubica1 (AUTHOR) ljubica.kuzmanovic@pmf.kg.ac.rs, Kovačević, Milan1 (AUTHOR) kovac@kg.ac.rs |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Sep2025, Vol. 50 Issue 3, p1-8. 8p. |
| Subjects: | Soils, Finite differences, Concentration gradient, Machine learning, Loss functions (Statistics), Boundary value problems, Diffusion kinetics |
| Abstract: | We investigate oxygen diffusion in the soil in one dimension by finite differences and the physics-informed neural network. Solving the diffusion equation by either method determines the oxygen concentration profiles inside the soil column at various times. However, while respecting specified Dirichlet and Neumann boundary conditions, the concentration profiles at certain times become negative, which is non-physical per se. We can resolve this situation in finite differences by proclaiming these negative concentration values as zero during the time-stepping scheme. In the case of PINN, we propose an innovative solution with a custom loss function, tailored to avoid such non-physical behavior. Two types of Dirichlet boundary conditions are investigated. The first is constant, and the second one periodically changes, with a period of 24 hours. We demonstrate that the PINN with a customized loss is effective and accurate. The proposed approach to circumvent non-physical solution areas demonstrates promise for application to various analogous problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186909527 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Treatment of non-physical solutions of the oxygen diffusion in soil by physics-informed neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ivanović%2C+Miloš%22">Ivanović, Miloš</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mivanovic@kg.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Savović%2C+Svetislav%22">Savović, Svetislav</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> savovic@kg.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Kuzmanović%2C+Ljubica%22">Kuzmanović, Ljubica</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ljubica.kuzmanovic@pmf.kg.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Kovačević%2C+Milan%22">Kovačević, Milan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kovac@kg.ac.rs</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Sep2025, Vol. 50 Issue 3, p1-8. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Soils%22">Soils</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+differences%22">Finite differences</searchLink><br /><searchLink fieldCode="DE" term="%22Concentration+gradient%22">Concentration gradient</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Boundary+value+problems%22">Boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+kinetics%22">Diffusion kinetics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We investigate oxygen diffusion in the soil in one dimension by finite differences and the physics-informed neural network. Solving the diffusion equation by either method determines the oxygen concentration profiles inside the soil column at various times. However, while respecting specified Dirichlet and Neumann boundary conditions, the concentration profiles at certain times become negative, which is non-physical per se. We can resolve this situation in finite differences by proclaiming these negative concentration values as zero during the time-stepping scheme. In the case of PINN, we propose an innovative solution with a custom loss function, tailored to avoid such non-physical behavior. Two types of Dirichlet boundary conditions are investigated. The first is constant, and the second one periodically changes, with a period of 24 hours. We demonstrate that the PINN with a customized loss is effective and accurate. The proposed approach to circumvent non-physical solution areas demonstrates promise for application to various analogous problems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences is the property of Springer Nature 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: 10.1007/s12046-025-02845-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 1 Subjects: – SubjectFull: Soils Type: general – SubjectFull: Finite differences Type: general – SubjectFull: Concentration gradient Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Loss functions (Statistics) Type: general – SubjectFull: Boundary value problems Type: general – SubjectFull: Diffusion kinetics Type: general Titles: – TitleFull: Treatment of non-physical solutions of the oxygen diffusion in soil by physics-informed neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ivanović, Miloš – PersonEntity: Name: NameFull: Savović, Svetislav – PersonEntity: Name: NameFull: Kuzmanović, Ljubica – PersonEntity: Name: NameFull: Kovačević, Milan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 50 – Type: issue Value: 3 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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