A Machine Learning approach for the validation and optimization of permittivity mixing rules for binary liquids

M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza

XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Caceres (Spain). 13-15 septiembre 2023

Summary:

This paper presents the application of Support Vector Regressor models trained with glycerin-water mixture signals from a Dielectric Resonator sensor. Each signal is labeled with a concentration considered. The performance of these models indicates which mixing rule fits the most with experimental permittivity values. Some modifications of these formulas are validated to acquire better estimations.

Publication date: September 2023.

Citation:
Monteagudo Honrubia, M., Herraiz-Martínez, F.J., Matanza, J., A Machine Learning approach for the validation and optimization of permittivity mixing rules for binary liquids, XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Caceres (Spain). 13-15 September 2023.

IIT-23-142C