Summary of Strategies For Pretraining Neural Operators, by Anthony Zhou et al.
Strategies for Pretraining Neural Operators
by Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian, Amir Barati Farimani
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the effects of pretraining on neural operators used in partial differential equation (PDE) modeling. Recent advances have shown that pretraining can improve generalizability and performance, but there is a need to compare and understand different pretraining frameworks. The study compares various pretraining methods without optimizing architecture choices, examining their impact on different models and datasets. The results show that pretraining depends heavily on model and dataset choices, but transfer learning or physics-based pretraining strategies tend to work best. Data augmentations can further improve pretraining performance, and pretraining is particularly beneficial when fine-tuning in scarce data regimes or generalizing to downstream data similar to the pretraining distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pretraining neural operators for PDE modeling has been shown to improve their ability to generalize and perform well across different datasets. However, it’s not clear how this works or which methods are most effective. This study compares different pretraining methods without changing the architecture of the models, to see what happens when they’re applied to different models and datasets. The results show that pretraining is closely tied to the specific model and dataset being used, but some strategies work better than others. For example, using transfer learning or physics-based pretraining can help improve performance. Additionally, adding noise to the training data can also make a big difference. |
Keywords
» Artificial intelligence » Fine tuning » Pretraining » Transfer learning
