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Uncertainty Analysis Based on Sensitivities Generated Using Automatic Differentiation

机译:基于自动分化产生的敏感性的不确定性分析

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The objective is to determine confidence limits for the outputs of a mathematical model of a physical system that consists of many interacting computer codes. Each code has many modules that receive inputs, write outputs, and depend on parameters. Several of the outputs of the system of codes can be compared to sensor measurements. The outputs of the system are uncertain because the inputs and parameters of the system are uncertain. The method uses sensitivities to propagate uncertainties from inputs to outputs through the complex chain of modules. Furthermore, the method consistently combines sensor measurements with model outputs to simultaneously obtain best estimates for model parameters and reduce uncertainties in model outputs. The method was applied to a test case where ADIFOR2 was used to calculate sensitivities for the radiation transport code MODTRAN.
机译:目的是确定由许多交互计算机代码组成的物理系统的数学模型的输出的置信限制。每个代码都有许多接收输入,写入输出并取决于参数的模块。可以将码系统的几个输出与传感器测量进行比较。系统的输出是不确定的,因为系统的输入和参数是不确定的。该方法使用敏感性传播来自输入的不确定性以通过复杂的模块输出。此外,该方法一致地将传感器测量与模型输出相结合,以同时获得模型参数的最佳估计,并降低模型输出中的不确定性。将该方法应用于测试案例,其中Adifor2用于计算辐射传输代码MODTRAN的敏感性。

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