SIAM/ASA Journal on Uncertainty Quantification, Volume 11, Issue 3, Page 1069-1104, September 2023.
Abstract. Computer model calibration is a crucial step in building a reliable computer model. In the face of massive physical observations, a fast estimation of the calibration parameters is urgently needed. To alleviate the computational burden, we design a two-step algorithm to estimate the calibration parameters by employing the subsampling techniques. Compared with the current state-of-the-art calibration methods, the complexity of the proposed algorithm is greatly reduced without sacrificing too much accuracy. We prove the consistency and asymptotic normality of the proposed estimator. The form of the variance of the proposed estimation is also presented, which provides a natural way to quantify the uncertainty of the calibration parameters. The obtained results of two numerical simulations and two real-case studies demonstrate the advantages of the proposed method.

中文翻译：

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具有大量物理观测的计算机模型的快速校准

SIAM/ASA 不确定性量化杂志，第 11 卷，第 3 期，第 1069-1104 页，2023 年 9 月。
抽象的。计算机模型校准是构建可靠的计算机模型的关键步骤。面对海量物理观测，迫切需要快速估计标定参数。为了减轻计算负担，我们设计了一种两步算法，通过采用子采样技术来估计校准参数。与当前最先进的校准方法相比，所提出的算法的复杂度大大降低，而不会牺牲太多的精度。我们证明了所提出的估计量的一致性和渐近正态性。还提出了所提出的估计的方差形式，这提供了一种量化校准参数的不确定性的自然方法。