Summary of Conformalized-deeponet: a Distribution-free Framework For Uncertainty Quantification in Deep Operator Networks, by Christian Moya et al.
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
by Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 paper develops a novel approach to quantify uncertainty in Deep Operator Network (DeepONet) regression models, building upon conformal prediction frameworks. The authors enhance their previously proposed Prob- and B-DeepONets by incorporating split conformal prediction, creating a distribution-free framework for generating rigorous confidence intervals. Additionally, they design a new Quantile-DeepONet that leverages split conformal prediction for natural uncertainty quantification. The effectiveness of the proposed methods is demonstrated through various numerical examples and multi-fidelity learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how deep neural networks can make predictions with confidence intervals. The researchers take an existing method called conformal prediction and combine it with their own deep learning models to get more accurate uncertainty estimates. They also create a new way of using this method that works well for certain types of problems. This approach could be useful in many areas where AI is used, like science, engineering, or finance. |
Keywords
* Artificial intelligence * Deep learning * Regression
