论文标题
周期性变化贝叶斯蒙特卡洛（Monte Carlo）用于有效的多模式后分布评估
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation
论文作者
论文摘要
由于不同的情况，例如某些环境条件的变化以及某些类型的损害和非线性，因此在工程中经常遇到某些基于物理模型参数的多模式分布。在统计模型更新中，对于本地可识别的参数，可以预见会发现多模式后验分布。这些多模式分布的完整表征非常重要，因为结构监测的方法论通常基于比较结构的受损和健康模型的比较。使用最先进的采样技术对后验多模式分布的表征将需要大量的基于物理模型的昂贵模拟。因此，如果可以在工程中进行有限数量的模拟，那么传统的采样技术将无法准确捕获多模式分布。在评估不确定性下的工程结构的性能时，这可能会导致大量数值错误。
Multimodal distributions of some physics based model parameters are often encountered in engineering due to different situations such as a change in some environmental conditions, and the presence of some types of damage and nonlinearity. In statistical model updating, for locally identifiable parameters, it can be anticipated that multi-modal posterior distributions would be found. The full characterization of these multi-modal distributions is important as methodologies for structural condition monitoring in structures are frequently based in the comparison of the damaged and healthy models of the structure. The characterization of posterior multi-modal distributions using state-of-the-art sampling techniques would require a large number of simulations of expensive to run physics-based models. Therefore, when a limited number of simulations can be run, as it often occurs in engineering, the traditional sampling techniques would not be able to capture accurately the multimodal distributions. This could potentially lead to large numerical errors when assessing the performance of an engineering structure under uncertainty.
