论文标题
使用稀疏的随机特征与应用程序中的应用中的无侵蚀替代建模
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis
论文作者
论文摘要
有效的替代建模是数据驱动的情况下不确定性量化的关键要求。在这项工作中，描述了一种新的方法，即描述了使用稀疏的随机特征与自我监督的维度降低结合使用的替代建模。将该方法与从崩溃分析获得的合成和真实数据的其他方法进行了比较。结果表明，此处描述的方法优于艺术替代建模技术，多项式混乱扩展和神经网络的优势。
Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.
