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

该项目将开发数据驱动的替代建模方法，以实现电机设计背景下的不确定性量化（UQ）研究。首先，随机场形式的材料和几何不确定性将被随机建模。接下来，将设计机器学习回归算法来近似感兴趣的电机量对不确定设计参数的依赖性。最后，基于代理的UQ方法的故障概率估计和多变量灵敏度分析，两个重要的和计算要求的UQ任务，将开发量化的不确定性的影响，提供新的见解，并促进改进的机器设计。

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.