Fundamental study of machine learning based inverse problem method for electromagnetic fields

Abstract

In contrast to forward analysis, which derives outputs from inputs, inverse analysis estimates inputs from outputs. Inverse analysis plays a crucial role in optimal design and efficient control; however, it is known to be an ill-posed problem that is difficult to solve. This study aims to construct a framework for electromagnetic inverse analysis assisted by machine learning, targeting estimation of the source current density distribution from the electric field distribution. In this paper, we report on the application of our proposed machine learning models to two-dimensional and three-dimensional electrostatic field problems, demonstrating a promising outlook for establishing this inverse analysis method.