Bayesian Structural Model Updating with Multimodal Variational Autoencoder
<|reference_start|>Bayesian Structural Model Updating with Multimodal Variational Autoencoder: A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to various dynamic analysis models. The proposed approach was benchmarked using a numerical model of a single-story frame building with acceleration and dynamic strain measurements. Additionally, an example involving a Bayesian update of nonlinear model parameters for a three-degree-of-freedom lumped mass model demonstrates computational efficiency when compared to using the original VAE, while maintaining adequate accuracy for practical applications.<|reference_end|>
@article{itoi2024bayesian,
    title={Bayesian Structural Model Updating with Multimodal Variational
  Autoencoder},
    author={Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee, Taro Yaoyama},
    journal={Computer Methods in Applied Mechanics and Engineering,Volume 429,
  1 September 2024, 117148},
    year={2024},
    doi={10.1016/j.cma.2024.117148},
    archivePrefix={arXiv},
    eprint={2406.09051},
    primaryClass={stat.ML cs.LG stat.AP}
}