基于最优预测元模型的灵敏度分析
Sensitivity analysis using the Metamodel of Optimal Prognosis
AI总结：
本文提出一种自动选择最优元模型并结合先进过滤技术降维的方法，通过基于元模型的灵敏度分析识别重要变量和最优子空间，从而在高维问题中实现高效且准确的概率分析。
摘要：
在虚拟原型设计过程的实际应用中，并不总能降低物理模型的复杂度并获得可快速求解的数值模型。通常，每一次数值模拟都需要数小时甚至数天。尽管数值方法和高性能计算不断进步，但在这种情况下仍无法探索各种模型配置，因此需要高效的代理模型。一般来说，现有的元模型技术根据所研究问题的不同，会表现出各自的优缺点。本文提出了一种自动选择适合当前问题的最优元模型的方法。结合使用先进过滤技术对变量空间进行自动降维，即使对于高维问题也能实现高效近似。这些过滤技术能够将高维变量空间缩减到一个更小的子空间，在该子空间中进行基于元模型的灵敏度分析，以评估重要变量的影响，并确定最优子空间及相应的代理模型，从而实现最准确的概率分析。为此，我们研究了基于方差的和无矩的灵敏度度量，并结合移动最小二乘和克里金等先进元模型。
英文摘要：
In real case applications within the virtual prototyping process, it is not always possible to reduce the complexity of the physical models and to obtain numerical models which can be solved quickly. Usually, every single numerical simulation takes hours or even days. Although the progresses in numerical methods and high performance computing, in such cases, it is not possible to explore various model configurations, hence efficient surrogate models are required. Generally the available meta-model techniques show several advantages and disadvantages depending on the investigated problem. In this paper we present an automatic approach for the selection of the optimal suitable meta-model for the actual problem. Together with an automatic reduction of the variable space using advanced filter techniques an efficient approximation is enabled also for high dimensional problems. This filter techniques enable a reduction of the high dimensional variable space to a much smaller subspace where meta-model-based sensitivity analyses are carried out to assess the influence of important variables and to identify the optimal subspace with corresponding surrogate model which enables the most accurate probabilistic analysis. For this purpose we investigate variance-based and moment-free sensitivity measures in combination with advanced meta-models as moving least squares and kriging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
