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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| Main Authors: | Alemayehu Tamirie Deresse, Tamirat Temesgen Dufera |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/3328977 |
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