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.
翻译:计算机模型校准是建立可靠计算机模型的关键步骤。 面对大规模物理观测,迫切需要快速估算校准参数。 为了减轻计算负担, 我们设计了两步算法, 使用子抽样技术估算校准参数。 与目前的最新校准方法相比, 提议的算法的复杂性大大降低, 同时又不牺牲太多的准确性。 我们证明了提议的测算法的一致性和无症状的正常性。 提议的估算也呈现了差异的形式, 提供了一种自然量化校准参数不确定性的方法。 两次数字模拟和两次实际案例研究的结果显示了拟议方法的优势 。