Efficient forward uncertainty propagation strategies in complex systems

由于计算能力的最新进展，计算机辅助工程（CAE）工具今天可以用来支持大规模的工程设计。CAE工具允许通过构建虚拟原型而不是构建多个昂贵的物理原型来快速探索设计的性能，并评估无法通过实验再现的操作条件。然而，这些虚拟原型产生的性能结果可能与通过测试实际结构获得的性能结果有很大不同。这可能是由制造可变性或缺乏知识引起的模型参数（如几何形状、材料属性和接头）的不确定性造成的。因此，近年来，广泛的研究一直致力于发展的分析方法，可以考虑到不确定性，开发鲁棒设计，这是相对不敏感的参数不确定性。尽管如此，通过大规模模型开发有效和准确的前向不确定性传播策略，（通常包括大量的不确定参数）仍然是这一研究领域的主要挑战之一，特别是当不确定参数使用混合概率和非概率不确定性描述建模时。（10天，2017年7月5日至2017年7月14日-不包括旅行时间）至位于阿尔伯克基（美国）的桑迪亚国家实验室的优化和不确定性定量部门。这笔小额旅行补助金将使Cicirello博士和优化和不确定性量化团队之间的密切合作能够深入研究这个问题，并促进和加速未来的合作项目。

Thanks to recent advances in computing capabilities, Computer-Aided-Engineering (CAE) tools can be used today to support large-scale engineering design. CAE tools allow to rapidly explore the performance of a design by building virtual prototypes, rather than building multiple expensive physical prototypes, and to assess operating conditions not reproducible with experiments.However, the performance results yielded by these virtual prototypes might be significantly different from those obtained by testing the actual structure. This can be caused by uncertainties model parameters (such as geometry, material properties, and joints) arising from manufacturing variability or lack of knowledge. As a result, in recent years extensive research has been devoted to the development of analysis methods which can take into account uncertainties to develop robust designs which are relatively insensitive to parameter uncertainties. Nonetheless, developing efficient and accurate forward uncertainty propagation strategies through large scale models (which usually include a large number of uncertain parameters) is still one of the major challenges in this research area, especially when uncertain parameters are modelled using mixed probabilistic and non-probabilistic uncertainty descriptions.This overseas travel grant application seeks to fund a short visit (10 days, 5 July 2017 to 14 July 2017 - excluding travel time) to the Optimization and Uncertainty Quantification department of Sandia National Laboratories in Albuquerque (US). This small travel grant will enable close collaboration between Dr Cicirello and the Optimization and Uncertainty Quantification team to work intensively on the problem, and to facilitate and accelerate future collaborative projects.