Summary of Discovering Physical Laws with Parallel Combinatorial Tree Search, by Kai Ruan et al.
Discovering physical laws with parallel combinatorial tree search
by Kai Ruan, Yilong Xu, Ze-Feng Gao, Yike Guo, Hao Sun, Ji-Rong Wen, Yang Liu
First submitted to arxiv on: 5 Jul 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 proposed parallel combinatorial tree search (PCTS) model efficiently distills generic mathematical expressions from limited data, outperforming state-of-the-art baseline models on over 200 synthetic and experimental datasets. By leveraging symbolic regression, PCTS discovers concise and interpretable mathematical formulas, addressing the long-standing challenge of finding parsimonious and generalizable mathematical formulas in an infinite search space while fitting training data accurately and efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PCTS is a new way to find simple math formulas from limited data. It’s really good at doing this, much better than other models that have been tried before. This helps scientists discover new laws and patterns across different fields of study. The model works by searching through many possible formulas quickly and accurately. |
Keywords
* Artificial intelligence * Regression
