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发现物理定律与并行符号的列举.
Kai Ruan1, Yilong Xu1, Ze-Feng Gao1
1Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China.
Nature computational science
|November 21, 2025
概括
平行符号计数 (PSE) 有效地从数据中发现可解释的数学模型. 这种新方法显著提高了复杂的符号回归任务的准确性和速度.
科学领域:
- 数据科学数据科学数据科学
- 计算科学 计算科学
- 科学发现 科学发现 发现
背景情况:
- 符号回归对于从数据中提取简洁,可解释的数学表达式至关重要.
- 一个主要的挑战是有效地在无限空间中搜索节的,可概括的公式.
- 当前的算法在复杂问题上的准确性和效率方面扎,从而减缓了科学探索.
研究的目的:
- 引入并行符号计数 (PSE) 以实现高效的,数据驱动的数学表达式发现.
- 解决现有的符号回归算法的精度和速度的局限性.
- 加强符号学习在跨学科科学领域的应用.
主要方法:
- 开发了并行符号计数 (PSE),这是一个新的符号回归算法.
- 设计的PSE可以有效地从有限的数据集中提取通用数学表达式.
- 专注于提高复杂的数据驱动发现任务的可扩展性和性能.
主要成果:
- 与最先进的方法相比,PSE证明了卓越的准确性和计算速度.
- 在恢复准确度方面实现了高达99%的改进,并且运行时间减少了数量级.
- 在200多个合成和实验问题集中验证了性能.
结论:
- PSE推进了象征性,可解释模型的准确和高效的数据驱动发现.
- 该方法在恢复潜在的物理定律和其他科学原理方面取得了显著的改进.
- PSE增强了符号学习的可扩展性和适用性,用于科学研究.
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