Exploring Integration of Surrogate Models Through a Case Study on Variable Frequency Drives

High-fidelity simulation models of variable frequency drives often incur expensive computation due to high granularity, complex physics and highly stiff components, hindering real-time Digital Twin Industry 4.0 applications. Surrogate models can outperform simulation solvers by orders of magnitude, potentially making real-time virtual drives feasible within practical computational limits. Despite this potential, current surrogate models suffer from limited generalizability and robustness. In this paper, we present an industrial case study exploring the combination of deep learning with surrogate modeling for simulating variable frequency drives, specifically replacing the induction motor high-fidelity component. We investigate the performance of Long-Short Term Memory-based surrogates, examining how their prediction accuracy and training time vary with synthetic datasets of different sizes, and how well the induction motor surrogates generalize across different motor resistances. This initial study aims to establish a foundation for further development, benchmarking and automation of surrogate modeling workflow for simulation enhancement.