物理信息神经网络法支持的某些海浪的孤子解决方案

Ismail Onder, Abdulkadir Sahiner, A. Seçer, Mustafa Bayram
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引用次数: 0

摘要

在本研究中,我们旨在通过物理信息神经网络(PINN)方法获得修正的本杰明-博纳-马霍尼方程、奥斯特洛夫斯基-本杰明-博纳-马霍尼方程和米哈伊洛夫-诺维科夫-王方程的数值结果。这些方程针对浅水和长水波以及海洋工程中的基本模型和现象模型建模。根据实现方法,我们获得了扭结、明亮、多孤子(双孤子)和暗-明混合孤子的 PINN 解。根据所得结果推断,与文献中的其他近似求解方法相比,我们在某些情况下取得了良好的结果。然而,我们也观察到,在孤子类型复杂且分层的情况下,我们无法获得最佳结果。在获得结果的同时,隐藏层的数量和层中神经网络的数量也各不相同。这些结果见表。由于已知上述模型无法用 PINN 方法求解,我们预计这项研究将引发海洋工程领域的其他研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Soliton Solutions of Some Ocean Waves Supported by Physics Informed Neural Network Method
In this study, we aim to obtain numerical results of the modified Benjamin-Bona-Mahony equation, Ostrovsky-Benjamin-Bona-Mahony equation and Mikhailov-Novikov-Wang equation via the physics-informed neural networks (PINN) method. The equations are modeled for shallow and long water waves, as well as fundamental and phenomenonal models in ocean engineering. According to the implementation, we obtained the PINN solutions of kink, bright, multisoliton (two-soliton) and mixed dark-bright soliton solutions. According to the inference from the obtained results, we achieved good results in some cases compared to other approximate solution methods in the literature. However, it was also observed that the best possible results could not be obtained in cases where the soliton type was intricate and layered. While the results were obtained, the number of hidden layers and the number of neural networks in the layers also varied. These results are shown in tables. Since it is known that the aforementioned models are not solved by the PINN method, we anticipate that the study will lead to other studies in the field of ocean engineering.
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