Uncertainty Quantification (UQ) is a booming discipline for complex computational models based on the analysis of robustness, reliability and credibility. UQ analysis for nonlinear crash models with high dimensional outputs presents important challenges. In crashworthiness, nonlinear structural behaviours with multiple hidden modes require expensive models (18 h for a single run). Surrogate models (metamodels) allow substituting the full order model, introducing a response surface for a reduced training set of numerical experiments. Moreover, uncertain input and large number of degrees of freedom result in high dimensional problems, which derives to a bottle neck that blocks the computational efficiency of the metamodels. Kernel Principal Component Analysis (kPCA) is a multidimensionality reduction technique for non-linear problems, with the advantage of capturing the most relevant information from the response and improving the efficiency of the metamodel. Aiming to compute the minimum number of samples with the full order model. The proposed methodology is tested with a practical industrial problem that arises from the automotive industry.

不确定性量化 (UQ) 是基于稳健性、可靠性和可信度分析的复杂计算模型的蓬勃发展的学科。具有高维输出的非线性碰撞模型的 UQ 分析提出了重要挑战。在耐撞性方面，具有多个隐藏模式的非线性结构行为需要昂贵的模型（单次运行 18 小时）。代理模型（元模型）允许替换全阶模型，为减少的数值实验训练集引入响应面。此外，不确定的输入和大量的自由度会导致高维问题，从而导致阻碍元模型计算效率的瓶颈。核主成分分析 (kPCA) 是一种针对非线性问题的多维降维技术，具有从响应中捕获最相关信息并提高元模型效率的优势。旨在使用全阶模型计算最小样本数。所提出的方法通过汽车行业产生的实际工业问题进行了测试。