本文用多层前向神经网络求解该非线性偏微分方程，从而逼近非线性系统的中心流形。
In this paper, multi-layer feedforward neural networks are used to solve the nonlinear partial differential equation, and approach the centre manifold of the nonlinear system.
它遵循的主要附加要求是，该多组分直达波激发包括前向散射场马尔·琴科方程的迭代解决方案。
It follows that the main additional requirement is that the multicomponent direct arrival, needed to initiate the iterative solution of the Marchenko equation, includes the forward-scattered field.
在光束传输法中只分析前向亥姆霍兹方程，而后向分量近似为零。
Only positive Helmholtz equation is considered in beam propagation method, while the negative component is approximately zero.
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