volume | PIER Journals

Abstract

In this paper, a novel framework is proposed which combines physical scattering models with artificial neural networks (ANN) to solve electromagnetic scattering problems of random media through a volume integral equation formulation. The framework is applied to a snow scattering problem where snow is represented by a bicontinuous random medium. A neural network is constructed linking the random media structure to the induced dipole moments on the media. The volume integral equation (VIE) serves as a natural physical constraint on the network input-out relations and is used to guide the training of the network. A discrete dipole approximation (DDA) strategy is adopted to convert the VIE into matrix equations which also defines the loss function of the surrogate neural network. For addressing deterministic scattering problems, this represents a viable alternative to traditional iterative algorithms, providing comparable accuracy at the expense of reduced efficiency. In solving statistical scattering problems, neural networks with physics-informed loss function achieve accuracy comparable to that of data-driven models while significantly reducing the dependency on extensive precomputed training datasets. The physics-based loss function also allows the network to self-diagnose the prediction accuracy in real operations. This work demonstrates a novel strategy to effectively merge physical equations with artificial neural networks, and the idea can be inspiring to many relevant fields, especially when randomness effects are exhibited through a complicated nonlinear system.

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Dr. Jiayi Du

Mr. Yuanhao Cao

Mr. Chunzeng Luo

Dr. Gaoang Wang

Professor Shurun Tan