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.
@article{arxiv.2504.04480,
title = {Fine Tuning a Simulation-Driven Estimator},
author = {Braghadeesh Lakshminarayanan and Margarita A. Guerrero and Cristian R. Rojas},
journal= {arXiv preprint arXiv:2504.04480},
year = {2026}
}
Comments
Published in IEEE Control Systems Letters, vol. 9, pp 2975-2980, 2025