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Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

机译：使用贝叶斯推理和敏感性分析的不确定性降低：NASA Langley不确定性量化挑战的一种顺序方法

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摘要

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic uncertainty, and develops a rigorous methodology for efficient refinement of epistemic uncertainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refinement methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensitivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level performance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.
机译：本文提出了不确定性表征和传播的计算框架，以及在存在不确定性和认识论不确定性的情况下的敏感性分析，并开发了一种严密的方法，通过识别对整个工程系统的整体性能有重大影响的重要认识变量，有效地优化了不确定性。 。使用NASA兰利不确定性量化挑战（NASA-LUQC）问题说明了所提出的方法，该问题处理了通用运输模型（GTM）的不确定性分析。首先，贝叶斯推断用于使用子系统级别的模型和相应的数据来推断子系统级别的认知量。其次，使用基于方差的全局敏感性分析工具来识别四个重要的重要变量（NASA-LUQC中指定的此限制反映了实际工程情况，由于时间/预算约束，并非所有敏感变量都可以得到完善）影响系统级性能。本文最重要的贡献是开发了顺序优化方法，该方法无法一次识别出用于优化的认知变量。取而代之的是，首先仅识别一个变量，然后重复进行贝叶斯推断和全局敏感性计算以识别下一个重要变量。继续执行此过程，直到识别出所有4个变量并计算出系统级性能的提高。解释了所提出的顺序优化方法相对于一次性不确定性优化方法的优势，然后将其应用于NASA兰利不确定性量化挑战。

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    《》|2016年|351-362|共12页
  • 会议地点 San Diego CA(US)
  • 作者

    Shankar Sankararaman;

  • 作者单位

    SGT Inc. NASA Ames Research Center Moffett Field CA 94035 USA;

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