误差控制自适应克立格法中主动学习和知识共享的信息分析价值
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
摘要：
许多现象的巨大不确定性对决策提出了挑战。收集额外的信息，以更好地描述可减少的不确定性是决策的替代方案。信息价值（VoI）分析是一个数学决策框架，它量化了新数据的预期潜在效益，并有助于信息收集资源的最佳分配。然而，VoI的分析是计算非常昂贵的，因为基本的贝叶斯推断，特别是对于平等类型的信息。本文提出了第一个基于代理的VoI分析框架。而不是建模的极限状态函数描述的事件的兴趣决策，这是通常追求的替代模型为基础的可靠性方法，所提出的框架模型系统的响应。这种方法提供了从代理模型之间的观察共享平等型信息，以更新多个感兴趣的事件的可能性。此外，两个知识共享计划，称为模型和训练点共享，提出了最有效地利用昂贵的模型评估所提供的知识。这两种方案都集成了基于误差率的自适应训练方法，以有效地生成准确的克里格代理模型。建议的VoI分析框架适用于最佳决策问题，涉及桁架桥的负载测试。虽然国家的最先进的方法的基础上的重要性抽样和自适应克里格蒙特卡罗模拟无法解决这个问题，所提出的方法被证明是提供准确和强大的估计的VoI与有限数量的模型评估。因此，所提出的方法有利于应用VoI复杂的决策问题。
英文摘要：
Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and assists with optimal allocation of resources for information collection. However, analysis of VoI is computational very costly because of the underlying Bayesian inference especially for equality-type information. This paper proposes the first surrogate-based framework for VoI analysis. Instead of modeling the limit state functions describing events of interest for decision making, which is commonly pursued in surrogate model-based reliability methods, the proposed framework models system responses. This approach affords sharing equality-type information from observations among surrogate models to update likelihoods of multiple events of interest. Moreover, two knowledge sharing schemes called model and training points sharing are proposed to most effectively take advantage of the knowledge offered by costly model evaluations. Both schemes are integrated with an error rate-based adaptive training approach to efficiently generate accurate Kriging surrogate models. The proposed VoI analysis framework is applied for an optimal decision-making problem involving load testing of a truss bridge. While state-of-the-art methods based on importance sampling and adaptive Kriging Monte Carlo simulation are unable to solve this problem, the proposed method is shown to offer accurate and robust estimates of VoI with a limited number of model evaluations. Therefore, the proposed method facilitates the application of VoI for complex decision problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
