Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation
This work addresses a bottleneck in engineering applications, such as structural health monitoring, where limited simulations hinder accurate uncertainty assessment, though it is incremental as it builds on existing variational and Monte Carlo techniques.
The paper tackles the challenge of efficiently characterizing multi-modal posterior distributions in physics-based models, which is critical for structural condition monitoring but computationally expensive with traditional sampling methods. It introduces Cyclical Variational Bayes Monte Carlo to reduce the required number of simulations, achieving a 60% reduction in computational cost while maintaining accuracy.
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.