Summary of Bayesian Structural Model Updating with Multimodal Variational Autoencoder, by Tatsuya Itoi et al.
Bayesian Structural Model Updating with Multimodal Variational Autoencoder
by Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee, Taro Yaoyama
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel framework for updating Bayesian structural models by utilizing the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method approximates the likelihood when dealing with small numbers of observations and is suitable for high-dimensional correlated simultaneous observations. The proposed approach was benchmarked using a numerical model of a single-story frame building, demonstrating computational efficiency compared to using the original VAE while maintaining accuracy for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to update complex models that describe how things move or change over time. It uses special algorithms and computer programs called variational autoencoders (VAEs) to make this process more efficient and accurate. The method was tested on a simple building model, showing that it can be faster and just as good as the original approach. |
Keywords
» Artificial intelligence » Likelihood » Variational autoencoder
