Second-Order Adjoint Sensitivity Analysis Methodology for Computing Exactly Response Sensitivities to Uncertain Parameters and Boundaries of Linear Systems: Mathematical Framework

D. Cacuci
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引用次数: 5

Abstract

This work presents the “Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology (2nd-CASAM)” for the efficient and exact computation of 1st- and 2nd-order response sensitivities to uncertain parameters and domain boundaries of linear systems. The model’s response (i.e., model result of interest) is a generic nonlinear function of the model’s forward and adjoint state functions, and also depends on the imprecisely known boundaries and model parameters. In the practically important particular case when the response is a scalar-valued functional of the forward and adjoint state functions characterizing a model comprising N parameters, the 2nd-CASAM requires a single large-scale computation using the First-Level Adjoint Sensitivity System (1st-LASS) for obtaining all of the first-order response sensitivities, and at most N large-scale computations using the Second-Level Adjoint Sensitivity System (2nd-LASS) for obtaining exactly all of the second-order response sensitivities. In contradistinction, forward other methods would require (N2/2 + 3 N/2) large-scale computations for obtaining all of the first- and second-order sensitivities. This work also shows that constructing and solving the 2nd-LASS requires very little additional effort beyond the construction of the 1st-LASS needed for computing the first-order sensitivities. Solving the equations underlying the 1st-LASS and 2nd-LASS requires the same computational solvers as needed for solving (i.e., “inverting”) either the forward or the adjoint linear operators underlying the initial model. Therefore, the same computer software and “solvers” used for solving the original system of equations can also be used for solving the 1st-LASS and the 2nd-LASS. Since neither the 1st-LASS nor the 2nd-LASS involves any differentials of the operators underlying the original system, the 1st-LASS is designated as a “first-level” (as opposed to a “first-order”) adjoint sensitivity system, while the 2nd-LASS is designated as a “second-level” (rather than a “second-order”) adjoint sensitivity system. Mixed second-order response sensitivities involving boundary parameters may arise from all source terms of the 2nd-LASS that involve the imprecisely known boundary parameters. Notably, the 2nd-LASS encompasses an automatic, inherent, and independent “solution verification” mechanism of the correctness and accuracy of the 2nd-level adjoint functions needed for the efficient and exact computation of the second-order sensitivities.
精确计算线性系统对不确定参数和边界响应灵敏度的二阶伴随灵敏度分析方法:数学框架
这项工作提出了“二阶综合伴随灵敏度分析方法(2 - casam)”,用于有效和精确地计算线性系统对不确定参数和域边界的一阶和二阶响应灵敏度。模型的响应(即感兴趣的模型结果)是模型的前向和伴随状态函数的一般非线性函数,并且还取决于不精确已知的边界和模型参数。在实际重要的特殊情况下,当响应是包含N个参数的模型的前向和伴随状态函数的标值泛函时,二阶casam需要使用一级伴随灵敏度系统(first- lass)进行一次大规模计算,以获得所有一阶响应灵敏度。并使用二级伴随灵敏度系统(second- lass)进行了最多N次的大规模计算,以准确地获得所有的二阶响应灵敏度。相比之下,其他正向方法需要(N2/2 + 3 N/2)大规模计算才能获得所有一阶和二阶灵敏度。这项工作还表明,除了计算一阶灵敏度所需的第一阶lass的构建之外,构建和求解第二阶lass只需要很少的额外努力。求解第一lass和第二lass的方程需要与求解(即“反演”)初始模型下的正向或伴随线性算子相同的计算解算器。因此,用于求解原方程组的相同计算机软件和“求解器”也可以用于求解第一类和第二类lass。由于第1 - lass和第2 - lass都不涉及原始系统底层算子的任何微分,因此第1 - lass被指定为“一级”(而不是“一阶”)伴随灵敏度系统,而第2 - lass被指定为“二级”(而不是“二阶”)伴随灵敏度系统。涉及边界参数的混合二阶响应灵敏度可能出现在涉及不精确已知边界参数的所有二阶响应项中。值得注意的是,第二级lass包含了一个自动的、固有的、独立的“解验证”机制,用于有效和精确地计算二阶灵敏度所需的第二级伴随函数的正确性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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