A probabilistic metric for the validation of computational models
A probabilistic metric for the validation of computational models
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DOI：
10.1098/rsos.180687
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发表时间：
2018-11-01
影响因子：
3.5
通讯作者：
Patterson, Eann A.
中科院分区：
文献类型：
--
作者：
Dvurecenska, Ksenija;Graham, Steve;Patterson, Eann A.
提出了一种新的验证度量，该度量结合了基于测量数据中的不确定性的阈值的使用与归一化相对误差，并且在数据中存在较大变化的情况下是鲁棒的。该度量的结果是模型的预测代表真实的世界的概率，该概率基于与从其获取测量的实验有关的特定条件和置信水平。相对误差度量传统上被设计用于一系列数据值，但正交分解已被用来减少数据矩阵的特征向量的维数，使度量可以应用于数据的字段。三个以前发表的案例研究，以证明这种定量方法的有效性，在学科的结构分析，历史数据可供验证过程中，然而，这个概念可以应用到广泛的学科和部门的建模和模拟发挥了关键作用。
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalized relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with a series of data values, but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data were available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation play a pivotal role.