论文标题
具有大量物理观察的计算机模型的快速校准
Fast Calibration for Computer Models with Massive Physical Observations
论文作者
论文摘要
计算机模型校准是构建可靠的计算机模型的关键步骤。面对大量的物理观察，迫切需要对校准参数进行快速估计。为了减轻计算负担，我们设计了一种两步算法来通过采用子采样技术来估计校准参数。与当前的最新校准方法相比，所提出的算法的复杂性大大降低而不牺牲过多的准确性。我们证明了所提出的估计量的一致性和渐近正态性。还介绍了所提出的估计的方差形式，这提供了一种自然的方法来量化校准参数的不确定性。获得的两个数值模拟的结果和两项实际案例研究证明了该方法的优势。
Computer model calibration is a crucial step in building a reliable computer model. In the face of massive physical observations, a fast estimation for 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.
