Predicting the Number of Line Sources Inside a Cylinder Using Classification Neural Networks

This study addresses the inverse problem of iden-tifying the number of line sources exciting a dielectric cylinder using machine learning. Solving the direct problem generates the required training data, enabling a classification neural network to accurately predict the number of sources considering a limited dataset. Demonstrating effectiveness, this work contributes to advancing numerical methods for inverse source problems and highlights machine learning's potential for precise outcomes.