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,通过计算机模拟在许多工程和科学学科中用于分析。最近,这种方法已经引入到了计算医学中,即数字医疗孪生(DTH)的概念中。这种研究需要验证和验证其模型,以及相应的敏感性分析和不确定性量化(VVUQ)。从计算角度来看,VVUQ是一个计算密集型的过程,因为它需要对输入参数进行多次运行。研究人员经常使用高性能计算(HPC)解决方案来运行VVUQ研究,其中参数组合的数量可以轻松达到数万个。然而,对于大量的计算模型,还有一种可行的替代方案-无服务器计算。在本文中,我们假设使用无服务器计算模型可以是运行选定的VVUQ计算的实用和高效的方法。我们在EasyVVUQ库的示例上展示了这一点,我们通过提供对许多无服务器服务的支持来扩展该库。使用适用于无服务器执行的计算医学领域中的两个真实应用程序评估了结果库-CloudVVUQ。我们的实验证明了所提出方法的可扩展性。