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Automating Embedded Analysis Capabilities and Managing Software Complexity in Multiphysics Simulation, Part II: Application to Partial Differential Equations

机译:在多物理场仿真中自动化嵌入式分析功能并管理软件复杂性,第二部分:在偏微分方程中的应用

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A template-based generic programming approach was presented in Part I of this series of papers [Sci. Program. 20 (2012), 197–219] that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations (PDEs). We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertainty quantification results for a 3D PDE application.
机译:本系列文章的第一部分介绍了基于模板的通用编程方法。程序。 20(2012),197–219]将编程物理模型的开发工作与计算嵌入式分析算法所需的其他数量(例如导数)的开发工作分开了。在本文中,我们描述了使用基于模板的通用编程方法来模拟和分析偏微分方程(PDE)的实现细节。我们详细介绍了我们遇到的一些障碍,以及为克服这些障碍而开发的一些软件基础结构。我们以演示结尾,在此我们展示3D PDE应用程序的形状优化和不确定性量化结果。

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