Summary of Mad-scientist: Ai-based Scientist Solving Convection-diffusion-reaction Equations Using Massive Pinn-based Prior Data, by Mingu Kang et al.
MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data
by Mingu Kang, Dongseok Lee, Woojin Cho, Jaehyeon Park, Kookjin Lee, Anthony Gruber, Youngjoon Hong, Noseong Park
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates whether scientific foundation models (SFMs) can benefit from pre-training with noisy prior data, similar to large language models (LLMs). The authors use a PINN-based approach to collect approximated prior data in the form of PDE solutions and then utilize Transformer architectures to predict these solutions without knowing the governing equations. Experimental results on the one-dimensional convection-diffusion-reaction equation show that pre-training with approximated prior data remains robust, with only minor impacts on test accuracy. This finding opens up opportunities for pre-training SFMs with realistic, low-cost data instead of or in conjunction with numerical high-cost data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to figure out if they can make computers smarter by giving them some rough ideas about how things work. They used a special kind of math problem to test this idea and found that it works pretty well. This means that scientists might be able to teach computers new skills without having to give them a lot of information, just like how humans learn from experience. |
Keywords
» Artificial intelligence » Diffusion » Transformer
