Summary of Llm4ed: Large Language Models For Automatic Equation Discovery, by Mengge Du et al.
LLM4ED: Large Language Models for Automatic Equation Discovery
by Mengge Du, Yuntian Chen, Zhongzheng Wang, Longfeng Nie, Dongxiao Zhang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC); Mathematical Physics (math-ph); Applications (stat.AP)
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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 A new framework for discovering physical laws from data uses natural language-based prompts to guide large language models (LLMs) in automatically mining governing equations. The approach generates diverse equations using LLMs and evaluates them based on observations. An optimization phase is proposed, alternating between two strategies: using LLMs as a black-box optimizer and instructing them to perform evolutionary operators for global search. Experiments are conducted on partial differential equations and ordinary differential equations, demonstrating the framework’s ability to discover effective equations and reveal underlying physical laws. The results compare favorably with state-of-the-art models, showing good stability and usability. This framework has the potential to lower barriers to learning and applying equation discovery techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find math rules from data uses special computer programs called large language models (LLMs) to help discover equations that describe how things work. The method creates many different equations using LLMs and checks them against what we know about the world. To make sure the best equations are found, two strategies are used: one where LLMs are like a super smart calculator and another where they do a kind of search to find the best equation. Tests were run on special types of math problems called partial differential equations and ordinary differential equations. The results showed that this new way can help us discover good equations and understand how things work. This is important because it could make it easier for people to learn about and use this technique. |
Keywords
» Artificial intelligence » Optimization
