Summary of Efficient Training Of Deep Neural Operator Networks Via Randomized Sampling, by Sharmila Karumuri et al.
Efficient Training of Deep Neural Operator Networks via Randomized Sampling
by Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); 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 This paper proposes an innovative approach to improve the generalization ability and reduce computational time for DeepONet, a popular neural operator architecture used in real-time prediction of complex dynamics across various scientific and engineering applications. The proposed method involves random sampling during training, which targets the trunk network that outputs basis functions corresponding to spatiotemporal locations. This technique mitigates limitations of traditional uniform grid-based evaluation, leading to improved generalization and reduced memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve a powerful tool called DeepONet, used for predicting complex things like how water moves or what will happen in the weather. Right now, it takes a long time and uses too much computer power. The scientists found a way to make it work better by randomly choosing some of the points where they test the model’s predictions. This makes the model more accurate and faster, which is great news for people who use DeepONet to study things like climate change or how to design new materials. |
Keywords
» Artificial intelligence » Generalization » Spatiotemporal
