Data-driven Surrogate Modelling and Uncertainty Quantification for Electric Machines (D01)

该项目将开发数据驱动的代理建模方法，以便在电机设计的背景下进行不确定性量化(UQ)研究。首先，将对随机场形式的材料和几何不确定性进行随机建模。下一步，将设计机器学习回归算法来近似电机感兴趣的数量对不确定设计参数的依赖关系。最后，将开发基于代理的故障概率估计和多变量灵敏度分析的UQ方法，这两个重要且计算要求高的UQ任务将量化不确定性的影响，提供新的见解，并促进改进机器设计。

This project will develop data-driven surrogate modelling methods to enable uncertainty quantification (UQ) studies within the context of electric machine design. First, material and geometrical uncertainties in the form of random fields will be stochastically modelled. Next, machine learning regression algorithms will be designed to approximate the dependence of electric machine quantities of interest on uncertain design parameters. Last, surrogate-based UQ methods for failure probability estimation and multivariate sensitivity analysis, two important and computationally demanding UQ tasks, will be developed to quantify the impact of uncertainty, provide novel insights, and facilitate improved machine designs.