Machine Learning-Based Surrogate Modeling for Electromagnetic Damping Force Prediction and Parameter Ranking

This research investigates the influence of geometric, magnetic, and operating parameters on electromagnetic damping force in vibration damping systems. A structured dataset will be constructed from published experimental studies, and machine learning-based surrogate models will be developed to predict damping force and evaluate parameter importance. Feature importance techniques will be used to identify the most influential design variables, enabling improved interpretability and faster design insights for electromagnetic damping systems.