论文标题
通过主动学习和知识共享的信息分析价值在错误控制的自适应Kriging中
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging
论文作者
论文摘要
在许多现象中，大型不确定性挑战了决策。收集其他信息以更好地表征可简化的不确定性是决策替代方案。信息价值（VOI）分析是一个数学决策框架，可量化新数据的预期潜在好处，并有助于最佳的信息收集资源分配。但是，由于基本的贝叶斯推论，特别是对于平等类型的信息，对VOI的分析非常昂贵。本文提出了第一个基于替代的VOI分析框架。而不是对限制状态函数进行建模，以描述决策事件的限制函数，而决策事件通常是在基于替代模型的可靠性方法中进行的，而是提出的框架模型系统响应。这种方法可从替代模型之间的观察结果中共享平等型信息，以更新多个感兴趣的事件的可能性。此外，提出了两个称为模型的知识共享方案，并提出了培训点共享，以最有效地利用昂贵的模型评估提供的知识。这两种方案均与基于错误率的自适应训练方法集成在一起，以有效地生成准确的Kriging替代模型。提出的VOI分析框架用于最佳决策问题，涉及桁架桥的负载测试。尽管基于重要性采样和自适应Kriging Monte Carlo模拟的最新方法无法解决此问题，但建议的方法显示出具有有限数量的模型评估的VOI的准确且可靠的估计。因此，提出的方法促进了VOI在复杂决策问题中的应用。
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.
