Machine Learning-Based Dielectric Constant Measurement Method

Accurately predicting dielectric constants is essential in various fields. However, current microwave measurement methods often fail to utilize sensor data fully. Data-driven artificial neural networks can better identify correlations between output signals and sample features. This study proposes an approach that collects data through model simulations and trains artificial neural networks to predict dielectric constants for test materials more accurately. This research provides valuable insights for practical dielectric constant measurement and prediction applications.