论文标题
物理参数校准
Physical Parameter Calibration
论文作者
论文摘要
计算机仿真模型被广泛用于研究复杂的物理系统。一个相关的基本话题是反问题，也称为校准，旨在根据观察值了解模型中参数的值。在大多数实际应用中，参数具有特定的物理含义，我们称它们为物理参数。要识别真正的潜在物理系统，我们需要有效估计此类参数。但是，由于模型可识别性问题，现有的校准方法无法做到这一点。本文提出了一个半参数模型，称为差异分解模型，以描述物理系统和计算机模型之间的差异。提出的模型具有明确的解释，更重要的是，在轻度条件下可以识别出来。在此模型下，我们介绍了物理参数和差异的估计值，然后建立它们的渐近特性。数值示例表明，所提出的方法比现有方法更好地估计物理参数。
Computer simulation models are widely used to study complex physical systems. A related fundamental topic is the inverse problem, also called calibration, which aims at learning about the values of parameters in the model based on observations. In most real applications, the parameters have specific physical meanings, and we call them physical parameters. To recognize the true underlying physical system, we need to effectively estimate such parameters. However, existing calibration methods cannot do this well due to the model identifiability problem. This paper proposes a semi-parametric model, called the discrepancy decomposition model, to describe the discrepancy between the physical system and the computer model. The proposed model possesses a clear interpretation, and more importantly, it is identifiable under mild conditions. Under this model, we present estimators of the physical parameters and the discrepancy, and then establish their asymptotic properties. Numerical examples show that the proposed method can better estimate the physical parameters than existing methods.
