An Efficient and Accurate 2W-PE Method for Solving Spatial Field Strength Based on CVNN and PINN

Abstract

A two-way parabolic equation (2W-PE) method based on complex-valued neural networks (CVNNs) and a physics-informed neural network (PINN) is proposed to predict the spatial field in the environment of dielectric obstacles with high efficiency and accuracy. In the framework of the 2W-PE method, CVNNs are used to calculate the lumped transmission field and the lumped reflection field considering the influence of the obstacle, thus avoiding the long calculation time caused by the internal multilayered reflection processes. The incident directions and field strength of the waves on the regional boundaries vary greatly with the propagation environment, so coefficients of the boundary conditions are obtained by using the PINN. Next, the training results are applied to the examples using the continuation method and compared with the numerical results of the method of moments (MoM). The proposed 2W-PE method has high computational accuracy and efficiency, which reflects the applicability of machine learning in solving the computational efficiency problem of radio wave propagation. Therefore, this study provides a very effective and reliable method for solving the spatial field in the obstacle environment.