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.

翻译：计算机模拟模型被广泛用于研究复杂的物理系统。 与此相关的一个基本主题是反向问题,也称为校准,目的是了解基于观测的模型中的参数值。 在大多数实际应用中,参数具有具体的物理含义,我们称之为物理参数。为了确认真正的物理系统,我们需要有效地估计这些参数。然而,由于模型的可识别性问题,现有的校准方法无法很好地做到这一点。本文件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。拟议的模型有清晰的解释,更重要的是,可以在温和的条件下识别。在这个模型下,我们提出物理参数和差异的估测,然后确定这些参数的无现成性。数字实例表明,拟议的方法比现有方法更好地估计物理参数。