论文标题
使用BNNM用于干扰GBS样方程的干扰波解，并与PINN进行比较
Use of BNNM for interference wave solutions of the gBS-like equation and comparison with PINNs
论文作者
论文摘要
在这项工作中，普遍的孤子样（GBS样）方程是通过广义双线性方法得出的。可以找到可以符合零误差的显式解决方案的神经网络模型。使用双线性神经网络方法（BNNM）和物理知情神经网络（PINN）获得了类似GBS的方程的干扰波解。通过三维图和密度图很好地显示了干扰波。与Pinn相比，双线性神经网络方法不仅更准确，而且更快。
In this work, the generalized broken soliton-like (gBS-like) equation is derived through the generalized bilinear method. The neural network model, which can fit the explicit solution with zero error, is found. The interference wave solution of the gBS-like equation is obtained by using the bilinear neural network method (BNNM) and physical informed neural networks (PINNs). Interference waves are shown well via three-dimensional plots and density plots. Compared with PINNs, the bilinear neural network method is not only more accurate but also faster.
