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Can I read Data-driven solutions and parameter discovery of the nonlocal mKdV equation via deep learning method on EtoBox?
Data-driven solutions and parameter discovery of the nonlocal mKdV equation via deep learning method by Jinyan Zhu; Yong Chen is a Engineering article available to read on EtoBox.
What is Data-driven solutions and parameter discovery of the nonlocal mKdV equation via deep learning method about?
In this paper, we systematically study the integrability and data-driven solutions of the nonlocal mKdV equation. The infinite conservation laws of the nonlocal mKdV equation and the corresponding infinite conservation quantities are given through Riccti equation. The data-driven solutions of the zero boundary for the nonlocal mKdV equation are studied by using the multilayer physical information neural network algorithm, which include kink soliton, complex soliton, bright-bright soliton and the interaction between soliton and kink-type. For the data-driven solutions with nonzero boundary, we study kink, dark, anti-dark and rational solution. By means of image simulation, the relevant dynamic behavior and error analysis of these solutions are given. In addition, we discuss the inverse problem of the integrable nonlocal mKdV equation by applying the physics-informed neural net-
Who reads Data-driven solutions and parameter discovery of the nonlocal mKdV equation via deep learning method?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Jinyan Zhu; Yong Chen
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)