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.

翻译：当前许多行业部署高保真仿真器（数字孪生）来表征物理系统，但其参数必须经过校准以匹配真实系统。这推动了仿真驱动参数估计器的构建，其方法是为采样参数值生成合成观测数据，并学习从观测到参数的监督映射关系。然而，当真实参数超出采样范围时，预测会因分布外（OOD）误差而受到影响。本文针对两阶段估计器提出一种微调方法，以缓解OOD效应并提升估计精度。通过数值仿真验证了所提方法的有效性。