Complex Permittivity Reconstruction with Neural Networks

This project deals with an original method of determination of dielectric properties of materials, based minimally on measurements and heavily on modeling. Permittivity is reconstructed by a neural networking procedure matching measured and modeled characteristics of reflection. The experimental part of the method is implemented. Operation of the method is demonstrated through validation and determination of permittivity of liquid substances. Experimental and computational studies reveal important features of the current implementation and generate recommendations regarding its further development.