Academic Journal

Hybrid PINN-IFE approach for solving transmission problems in circular interface domains.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Hybrid PINN-IFE approach for solving transmission problems in circular interface domains.
Συγγραφείς: Azam, Muhammad, Alhwikem, Dalal, Ullah, Naseer
Πηγή: AIMS Mathematics; 2026, Vol. 11 Issue 4, p1-31, 31p
Θεματικοί όροι: Helmholtz equation, Finite element method, Numerical analysis, Theory of wave motion, Boundary value problems, Computational physics
Περίληψη: In this study, we proposed a hybrid methodology combining Physics-Informed Neural Networks (PINNs) and Immersed Finite Element (IFE) methods to address transmission problems in complex geometries, with a focus on Helmholtz-type equations. The technique addressed the challenge of solving wave equations in domains with circular interfaces, where material properties differ across the interface. The hybrid model leverages the strengths of PINNs to enforce the governing physical equations and IFE to provide a coarse initial solution, which is then corrected by the neural network using a signed-distance function to the interface. This correction was trained on a combination of supervised loss from data, physics-informed residual predictions, and interface conditions. Numerical experiments demonstrated high precision of the proposed technique when compared with manufactured exact solutions, achieving low error levels in both subdomains. High-frequency tests at ω = 100 further validated the method's accuracy and robustness across material configurations including normal and inverted contrast cases. Its mesh-free character provides flexibility and versatility for a wide range of transmission problems in computational physics, making it a promising method for solving interface-related issues in wave propagation. [ABSTRACT FROM AUTHOR]
Copyright of AIMS Mathematics is the property of American Institute of Mathematical Sciences 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.)
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  Data: Hybrid PINN-IFE approach for solving transmission problems in circular interface domains.
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  Data: <searchLink fieldCode="AR" term="%22Azam%2C+Muhammad%22">Azam, Muhammad</searchLink><br /><searchLink fieldCode="AR" term="%22Alhwikem%2C+Dalal%22">Alhwikem, Dalal</searchLink><br /><searchLink fieldCode="AR" term="%22Ullah%2C+Naseer%22">Ullah, Naseer</searchLink>
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  Data: AIMS Mathematics; 2026, Vol. 11 Issue 4, p1-31, 31p
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  Data: <searchLink fieldCode="DE" term="%22Helmholtz+equation%22">Helmholtz equation</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Theory+of+wave+motion%22">Theory of wave motion</searchLink><br /><searchLink fieldCode="DE" term="%22Boundary+value+problems%22">Boundary value problems</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+physics%22">Computational physics</searchLink>
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  Label: Abstract
  Group: Ab
  Data: In this study, we proposed a hybrid methodology combining Physics-Informed Neural Networks (PINNs) and Immersed Finite Element (IFE) methods to address transmission problems in complex geometries, with a focus on Helmholtz-type equations. The technique addressed the challenge of solving wave equations in domains with circular interfaces, where material properties differ across the interface. The hybrid model leverages the strengths of PINNs to enforce the governing physical equations and IFE to provide a coarse initial solution, which is then corrected by the neural network using a signed-distance function to the interface. This correction was trained on a combination of supervised loss from data, physics-informed residual predictions, and interface conditions. Numerical experiments demonstrated high precision of the proposed technique when compared with manufactured exact solutions, achieving low error levels in both subdomains. High-frequency tests at ω = 100 further validated the method's accuracy and robustness across material configurations including normal and inverted contrast cases. Its mesh-free character provides flexibility and versatility for a wide range of transmission problems in computational physics, making it a promising method for solving interface-related issues in wave propagation. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of AIMS Mathematics is the property of American Institute of Mathematical Sciences 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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      – Code: eng
        Text: English
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        PageCount: 31
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    Subjects:
      – SubjectFull: Helmholtz equation
        Type: general
      – SubjectFull: Finite element method
        Type: general
      – SubjectFull: Numerical analysis
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      – SubjectFull: Theory of wave motion
        Type: general
      – SubjectFull: Boundary value problems
        Type: general
      – SubjectFull: Computational physics
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      – TitleFull: Hybrid PINN-IFE approach for solving transmission problems in circular interface domains.
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            – D: 01
              M: 04
              Text: 2026
              Type: published
              Y: 2026
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