Bayesian Structural Model Updating with Multimodal Variational Autoencoder
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914841010110464 |
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| author | Itoi, Tatsuya Amishiki, Kazuho Lee, Sangwon Yaoyama, Taro |
| author_facet | Itoi, Tatsuya Amishiki, Kazuho Lee, Sangwon Yaoyama, Taro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Structural Model Updating with Multimodal Variational Autoencoder Itoi, Tatsuya Amishiki, Kazuho Lee, Sangwon Yaoyama, Taro Machine Learning Applications 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. |
| title | Bayesian Structural Model Updating with Multimodal Variational Autoencoder |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2406.09051 |