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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Veröffentlicht in:	Applied Computational Intelligence and Soft Computing Jg. 2024; H. 1
Hauptverfasser:	Deresse, Alemayehu Tamirie, Dufera, Tamirat Temesgen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Hindawi 2024 John Wiley & Sons, Inc Wiley
Schlagworte:	Accuracy Algorithms Approximation Artificial neural networks Boundary conditions Deep learning Differential equations Dirichlet problem Engineering Exact solutions Initial conditions Machine learning Neural networks Optimization Ordinary differential equations Partial differential equations Physics Propagation Stochastic models Germany
ISSN:	1687-9724, 1687-9732
Online-Zugang:	Volltext
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