Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation
The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/3328977 |
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| author | Alemayehu Tamirie Deresse Tamirat Temesgen Dufera |
| author_facet | Alemayehu Tamirie Deresse Tamirat Temesgen Dufera |
| author_sort | Alemayehu Tamirie Deresse |
| collection | DOAJ |
| description | The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation, encompassing Dirichlet and Neumann boundary conditions. To incorporate physical information for the sine-Gordon equation, a multiobjective loss function has been defined consisting of the residual of governing partial differential equation (PDE), initial conditions, and various boundary conditions. Using multiple densely connected independent artificial neural networks (ANNs), called feedforward deep neural networks designed to handle partial differential equations, PINNs have been trained through automatic differentiation to minimize a loss function that incorporates the given PDE that governs the physical laws of phenomena. To illustrate the effectiveness, validity, and practical implications of our proposed approach, two computational examples from the nonlinear sine-Gordon are presented. We have developed a PINN algorithm and implemented it using Python software. Various experiments were conducted to determine an optimal neural architecture. The network training was employed by using the current state-of-the-art optimization methods in machine learning known as Adam and L-BFGS-B minimization techniques. Additionally, the solutions from the proposed method are compared with the established analytical solutions found in the literature. The findings show that the proposed method is a computational machine learning approach that is accurate and efficient for solving nonlinear sine-Gordon equations with a variety of boundary conditions as well as any complex nonlinear physical problems across multiple disciplines. |
| format | Article |
| id | doaj-art-75bd79846a3a49efa22cb1edf26223e9 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-75bd79846a3a49efa22cb1edf26223e92025-08-20T03:35:32ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/3328977Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon EquationAlemayehu Tamirie Deresse0Tamirat Temesgen Dufera1Department of Applied MathematicsDepartment of Applied MathematicsThe nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation, encompassing Dirichlet and Neumann boundary conditions. To incorporate physical information for the sine-Gordon equation, a multiobjective loss function has been defined consisting of the residual of governing partial differential equation (PDE), initial conditions, and various boundary conditions. Using multiple densely connected independent artificial neural networks (ANNs), called feedforward deep neural networks designed to handle partial differential equations, PINNs have been trained through automatic differentiation to minimize a loss function that incorporates the given PDE that governs the physical laws of phenomena. To illustrate the effectiveness, validity, and practical implications of our proposed approach, two computational examples from the nonlinear sine-Gordon are presented. We have developed a PINN algorithm and implemented it using Python software. Various experiments were conducted to determine an optimal neural architecture. The network training was employed by using the current state-of-the-art optimization methods in machine learning known as Adam and L-BFGS-B minimization techniques. Additionally, the solutions from the proposed method are compared with the established analytical solutions found in the literature. The findings show that the proposed method is a computational machine learning approach that is accurate and efficient for solving nonlinear sine-Gordon equations with a variety of boundary conditions as well as any complex nonlinear physical problems across multiple disciplines.http://dx.doi.org/10.1155/2024/3328977 |
| spellingShingle | Alemayehu Tamirie Deresse Tamirat Temesgen Dufera Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation Applied Computational Intelligence and Soft Computing |
| title | Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation |
| title_full | Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation |
| title_fullStr | Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation |
| title_full_unstemmed | Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation |
| title_short | Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation |
| title_sort | exploring physics informed neural networks for the generalized nonlinear sine gordon equation |
| url | http://dx.doi.org/10.1155/2024/3328977 |
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