Uncertainty Quantification (UQ) is a key discipline for computational modeling of complex systems, enhancing reliability of engineering simulations. In crashworthiness, having an accurate assessment of the behavior of the model uncertainty allows reducing the number of prototypes and associated costs. Carrying out UQ in this framework is especially challenging because it requires highly expensive simulations. In this context, surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process. Different techniques to describe the metamodel are considered, Ordinary Kriging, Polynomial Response Surfaces and a novel strategy (based on Proper Generalized Decomposition) denoted by Separated Response Surface (SRS). A large number of uncertain input parameters may jeopardize the efficiency of the metamodels. Thus, previous to define a metamodel, kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description. A benchmark crash test is used to show the efficiency of combining metamodels with kPCA.

翻译：不确定量化(UQ)是复杂系统计算模型的关键学科,提高了工程模拟的可靠性。在不适性情况下,对模型不确定性的行为进行准确评估,可以减少原型数量和相关成本。在这个框架内实施UQ特别具有挑战性,因为它需要非常昂贵的模拟。在这方面,代用模型(元模型)可以大幅降低蒙特卡洛进程的计算成本。考虑到描述元模型的不同技术,普通克里吉、聚合反应表面和由独立反应表(SRS)表示的新颖战略(以适当通用分解为基础)。大量不确定的投入参数可能危及元模型的效率。因此,在确定元模型之前,发现内核主元元元分析(kPCA)能够有效简化模型结果描述。使用基准碰撞测试来显示将模型与KPCA合并的效率。