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.

不确定性量化（UQ）是复杂系统的计算建模的关键学科，可增强工程仿真的可靠性。在耐撞性方面，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。在此框架中执行UQ特别具有挑战性，因为它需要非常昂贵的仿真。在这种情况下，替代模型（元模型）可以大大降低蒙特卡洛过程的计算成本。考虑了描述元模型的不同技术，包括普通Kriging，多项式响应面和由分离响应面（SRS）表示的新策略（基于适当的广义分解）。大量不确定的输入参数可能会损害元模型的效率。因此，在定义元模型之前，发现内核主成分分析（kPCA）有效简化了模型结果描述。基准崩溃测试用于显示将元模型与kPCA组合的效率。