SIAM/ASA Journal on Uncertainty Quantification, Volume 8, Issue 4, Page 1287-1309, January 2020.
In the presence of model discrepancy, the calibration of physics-based models for physical parameter inference is a challenging problem. Lack of identifiability between calibration parameters and model discrepancy requires additional identifiability constraints to be placed on the model discrepancy to obtain unique physical parameter estimates. If these assumptions are violated, the inference for the calibration parameters can be systematically biased. In many applications, such as in dynamic material property experiments, many of the calibration inputs refer to measurement uncertainties. In this setting, we develop a metric for identifying overfitting of these measurement uncertainties, propose a prior capable of reducing this overfitting, and show how this leads to a diagnostic tool for validation of physical parameter inference. The approach is demonstrated for a benchmark example and applied for a material property application to perform inference on the equation of state parameters of tantalum.

中文翻译：

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在贝叶斯模型校准中将测量不确定度作为干扰参数处理

SIAM / ASA不确定性量化期刊，第8卷，第4期，第1287-1309页，2020年1月。
在存在模型差异的情况下，基于物理模型的物理参数推断的校准是一个具有挑战性的问题。校准参数与模型差异之间缺乏可识别性，因此需要在模型差异上附加其他可识别性约束，以获得唯一的物理参数估计值。如果违反了这些假设，则可以系统地对校准参数的推论产生偏差。在许多应用中，例如在动态材料特性实验中，许多校准输入涉及测量不确定性。在这种情况下，我们开发了一种度量来识别这些测量不确定性的过度拟合，提出了一种能够减少这种过度拟合的先验技术，并展示了这如何导致诊断工具验证物理参数推断。