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arXiv · 2504.04480

Fine Tuning a Simulation-Driven Estimator

Braghadeesh Lakshminarayanan·Margarita A. Guerrero·Cristian R. Rojas
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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.

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Braghadeesh Lakshminarayanan, Margarita A. Guerrero, Cristian R. Rojas. 2025-04-06. Fine Tuning a Simulation-Driven Estimator. https://doi.org/10.1109/lcsys.2025.3647070

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