Neural-Network Modeling for 3-D Substructures Based on Spatial EM-Field Coupling in Finite-Element Method.

This paper presents a new neural-network method to describe the electromagnetic (EM) behavior at the interface between the substructures from an internally decomposed EM structure. A set of neural networks is used to represent the EM behavior of the substructure as seen from the interface. This allows EM coupling between substructures to be effectively represented. The method is developed in a finite-element environment. An EM transfer function matrix is formulated to produce training data, allowing neural networks to learn the spatial coupling between EM-field variables at various locations over the interface of the substructure. A new formulation is proposed where trained neural networks are integrated into the finite-element equation for efficient simulation of an overall EM structure. A technique is developed to allow the proposed model to be used with the mesh different from that in neural-network training. Examples show that the proposed method provides better accuracy than conventional neural-network approaches for modeling substructures from an internally decomposed EM problem. Using the proposed model also speeds up finite-element simulation. [ABSTRACT FROM AUTHOR]