Fine Tuning a Simulation-Driven Estimator
arXiv:2504.04480 · doi:10.1109/LCSYS.2025.3647070
Abstract
Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations.
arXiv commentPublished in IEEE Control Systems Letters, vol. 9, pp 2975-2980, 2025