Digital twins are virtual representations of physical objects or systems used for the purpose of analysis, most often via computer simulations, in many engineering and scientific disciplines. Recently, this approach has been introduced to computational medicine, within the concept of Digital Twin in Healthcare (DTH). Such research requires verification and validation of its models, as well as the corresponding sensitivity analysis and uncertainty quantification (VVUQ). From the computing perspective, VVUQ is a computationally intensive process, as it requires numerous runs with variations of input parameters. Researchers often use high-performance computing (HPC) solutions to run VVUQ studies where the number of parameter combinations can easily reach tens of thousands. However, there is a viable alternative to HPC for a substantial subset of computational models - serverless computing. In this paper we hypothesize that using the serverless computing model can be a practical and efficient approach to selected cases of running VVUQ calculations. We show this on the example of the EasyVVUQ library, which we extend by providing support for many serverless services. The resulting library - CloudVVUQ - is evaluated using two real-world applications from the computational medicine domain adapted for serverless execution. Our experiments demonstrate the scalability of the proposed approach.

翻译：数字孪生是物理对象或系统的虚拟表示，在众多工程与科学学科中，通常通过计算机模拟用于分析目的。近年来，该方法已在“医疗数字孪生（DTH）”概念框架下引入计算医学领域。此类研究需要对其模型进行验证与确认，并开展相应的敏感性分析和不确定性量化（VVUQ）。从计算角度看，VVUQ是一个计算密集型过程，因为它需要大量运行不同输入参数组合的模拟。研究人员通常采用高性能计算（HPC）解决方案来执行VVUQ研究，其中参数组合的数量极易达到数万种。然而，对于相当一部分计算模型而言，存在一种可行的HPC替代方案——无服务器计算。本文提出假设：采用无服务器计算模型可以作为特定VVUQ计算场景中一种实用且高效的方法。我们以EasyVVUQ库为例进行验证，通过扩展该库以支持多种无服务器服务，开发出CloudVVUQ库。我们利用两个来自计算医学领域的真实应用对该库进行评估，并针对无服务器执行环境进行了适配。实验结果表明，所提出的方法具有良好的可扩展性。