论文标题
结构利用方法，用于通过异质介质的多相流中的快速不确定性定量
Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media
论文作者
论文摘要
我们提出了一个计算框架，用于降低尺寸和替代建模，以加速具有高维输入和功能值输出的计算密集型模型中的不确定性定量。在放射性废物存储的背景下，我们的驾驶应用程序是饱和多孔介质中的多相流。为了减少快速输入维度，我们利用一个近似的全局灵敏度度量，用于功能值输出，这是由主动子空间方法的想法所激发的。提出的方法不需要昂贵的梯度计算。我们通过将输出的截短的karhunen-loéve（KL）扩展与多项式混乱的扩展相结合，以生成有效的替代模型，用于在还原参数空间中构建的输出KL模式。我们通过一组全面的数值实验来证明所提出的替代建模方法的有效性，我们考虑了许多功能值（时间或空间分布）QOI。
We present a computational framework for dimension reduction and surrogate modeling to accelerate uncertainty quantification in computationally intensive models with high-dimensional inputs and function-valued outputs. Our driving application is multiphase flow in saturated-unsaturated porous media in the context of radioactive waste storage. For fast input dimension reduction, we utilize an approximate global sensitivity measure, for function-value outputs, motivated by ideas from the active subspace methods. The proposed approach does not require expensive gradient computations. We generate an efficient surrogate model by combining a truncated Karhunen-Loéve (KL) expansion of the output with polynomial chaos expansions, for the output KL modes, constructed in the reduced parameter space. We demonstrate the effectiveness of the proposed surrogate modeling approach with a comprehensive set of numerical experiments, where we consider a number of function-valued (temporally or spatially distributed) QoIs.
