Summary of Uncertainty Quantification For Deeponets with Ensemble Kalman Inversion, by Andrew Pensoneault et al.
Uncertainty quantification for deeponets with ensemble kalman inversion
by Andrew Pensoneault, Xueyu Zhu
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA); Machine Learning (stat.ML)
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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 The paper proposes a novel inference approach for efficient uncertainty quantification (UQ) in operator learning, specifically for DeepONets. The proposed method harnesses the power of Ensemble Kalman Inversion (EKI), which has shown advantages for UQ in physics-informed neural networks. The EKI-based approach enables training ensembles of DeepONets while obtaining informative uncertainty estimates for the output of interest. To accommodate larger datasets, a mini-batch variant is deployed to mitigate computational demands during training. Additionally, a heuristic method is introduced to estimate artificial dynamics covariance, improving uncertainty estimates. The methodology’s effectiveness and versatility are demonstrated across various benchmark problems, showcasing its potential to address UQ challenges in DeepONets for practical applications with limited and noisy data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem: it helps us learn complex things from limited data by showing how to figure out how wrong our predictions might be. This is important because we often need to know how likely it is that something will happen or not happen. The paper proposes a new way to do this, using an existing method called Ensemble Kalman Inversion (EKI). EKI helps us train many models at once and get good estimates of how wrong they might be. This makes it helpful for things like predicting weather patterns or medical test results. |
Keywords
* Artificial intelligence * Inference
