Summary of Bayesian Neural Networks For Predicting Uncertainty in Full-field Material Response, by George D. Pasparakis et al.
Bayesian neural networks for predicting uncertainty in full-field material response
by George D. Pasparakis, Lori Graham-Brady, Michael D. Shields
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Materials Science (cond-mat.mtrl-sci); 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 A machine learning-based framework is proposed for predicting stress fields in computational mechanics, addressing limitations of existing methods that lack uncertainty quantification for complex microstructures. The framework employs a modified Bayesian U-net architecture, which maps initial microstructure to stress field with prediction uncertainty estimates. Three inference algorithms are used: Hamiltonian Monte Carlo, Bayes by Backprop, and Monte-Carlo Dropout. A comparison of their predictive accuracy and uncertainty estimates is performed on a fiber-reinforced composite material and polycrystalline microstructure application. The results show high-accuracy predictions compared to FEA solutions, while uncertainty estimates depend on the inference approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to predict stress fields in materials using machine learning. This can help make predictions more accurate and faster than before. A special kind of AI model called a U-net is used to do this. The model is tested on two types of materials: one that has fibers and another that is made up of many tiny crystals. The results show that the predictions are very good, but the uncertainty around those predictions depends on how the AI model was trained. |
Keywords
* Artificial intelligence * Dropout * Inference * Machine learning
