Computational models are increasingly used for diagnosis and treatment of cardiovascular disease. To provide a quantitative hemodynamic understanding that can be effectively used in the clinic, it is crucial to quantify the variability in the outputs from these models due to multiple sources of uncertainty. To quantify this variability, the analyst invariably needs to generate a large collection of high-fidelity model solutions, typically requiring a substantial computational effort. In this paper, we show how an explicit-in-time ensemble cardiovascular solver offers superior performance with respect to the embarrassingly parallel solution with implicit-in-time algorithms, typical of an inner-outer loop paradigm for non-intrusive uncertainty propagation. We discuss in detail the numerics and efficient distributed implementation of a segregated FSI cardiovascular solver on both CPU and GPU systems, and demonstrate its applicability to idealized and patient-specific cardiovascular models, analyzed under steady and pulsatile flow conditions.

中文翻译：

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整体式FSI的整体求解器

计算模型越来越多地用于心血管疾病的诊断和治疗。为了提供可以在临床上有效使用的定量血流动力学理解，量化由于多种不确定性来源而导致这些模型输出的变异性至关重要。为了量化这种可变性，分析人员总是需要生成大量的高保真模型解决方案，这通常需要大量的计算工作。在本文中，我们展示了即时显式集合心血管求解器相对于采用隐式实时算法的尴尬并行解法（非侵入式不确定性传播的典型内外循环范式）如何提供卓越的性能。