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Defining predictive maturity for validated numerical simulations

机译:定义经过验证的数值模拟的预测成熟度

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The increasing reliance on computer simulations in decision-making motivates the need to formulate a commonly accepted definition for "predictive maturity." The concept of predictive maturity involves quantitative metrics that could prove useful while allocating resources for physical testing and code development. Such metrics should be able to track progress (or lack thereof) as additional knowledge becomes available and is integrated into the simulations for example, through the addition of new experimental datasets during model calibration, and/or through the implementation of better physics models in the codes. This publication contributes to a discussion of attributes that a metric of predictive maturity should exhibit. It is contended that the assessment of predictive maturity must go beyond the good-ness-of-fit of the model to the available test data. We firmly believe that predictive maturity must also consider the "knobs" or ancillary variables, used to calibrate the model and the degree to which physical experiments cover the domain of applicability. The emphasis herein is placed on translating the proposed attributes into mathematical properties, such as the degree of regularity and asymptotic limits of the maturity function. Altogether these mathematical properties define a set of constraints that the predictive maturity function must satisfy. Based on these constraints, we propose a Predictive Maturity Index (PMI). Physical datasets are used to illustrate how the PMI quantifies the maturity of the non-linear, Pres-ton-Tonks-Wallace model of plastic deformation applied to beryllium, a light-weight, high-strength metal. The question "does collecting additional data improve predictive power?" is answered by computing the PMI iteratively as additional experimental datasets become available. The results obtained reflect that coverage of the validation domain is as important to predictive maturity as goodness-of-fit. The example treated also indicates that the stabilization of predictive maturity can be observed, provided that enough physical experiments are available.
机译:决策过程中对计算机仿真的日益依赖激发了为“预测成熟度”制定公认的定义的需求。预测成熟度的概念涉及定量指标,这些指标可能证明在为物理测试和代码开发分配资源时很有用。这样的度量标准应该能够跟踪进度(或缺少进度),例如,通过在模型校准过程中添加新的实验数据集,和/或通过在模型中实施更好的物理模型,可以将其集成到模拟中。代码。该出版物有助于对可预测成熟度指标应表现出的属性进行讨论。有人认为,对预测成熟度的评估必须超出模型对现有测试数据的拟合优度。我们坚信,预测性成熟度还必须考虑用于校准模型的“旋钮”或辅助变量,以及物理实验涵盖适用范围的程度。本文的重点放在将建议的属性转换为数学属性,例如成熟度函数的规则程度和渐近极限。这些数学属性共同定义了预测成熟度函数必须满足的一组约束。基于这些约束,我们提出了预测成熟度指数(PMI)。物理数据集用于说明PMI如何量化应用于铍(一种轻质,高强度金属)的塑性变形的非线性Pres-ton-Tonks-Wallace模型的成熟度。问题“收集更多数据是否可以提高预测能力?”当其他实验数据集可用时,可以通过迭代计算PMI来回答。获得的结果表明,验证域的覆盖范围与拟合优度对于预测成熟度同样重要。所处理的示例还表明,只要有足够的物理实验可用,就可以观察到预测成熟度的稳定。

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