使用机器学习来找到精确的分析解决方案,以分析提出的物理问题
1Advanced ICT Institute, National Institute of Information and Communications Technology, 4-2-1, Nukuikitamachi, Koganei, Tokyo 184-8795, Japan.
Heliyon
|March 28, 2024
概括
机器学习,特别是符号回归 (SR),可以为理论物理问题找到精确的分析解决方案. 这种方法使用数值数据来发现潜在的数学函数,为复杂现象提供新的见解.
科学领域:
- 理论物理 理论物理
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 理论物理学中的分析问题往往缺乏简单的解决方案.
- 符号回归 (SR) 是一种新兴的机器学习技术,用于从数据中发现函数形式.
研究的目的:
- 研究符号回归在解决理论物理中的分析问题方面的有效性.
- 探索SR在发现精确,闭式解决方案和提供物理见解方面的潜力.
主要方法:
- 从分析物理问题 (例如,兰道-泽纳问题) 中生成数值数据.
- 使用最先进的符号回归包来识别生成数据中的功能关系.
主要成果:
- 成功地确定了已知的问题及其概括的确切解决方案.
- 证明了SR能够找到封闭形式的表达式,提供简洁的表示和直观理解的潜力.
- 突出了当前SR套餐的局限性,并提出了需要改进的领域.
结论:
- 机器学习,特别是符号回归,显示出解决理论物理学中分析提出的问题的巨大潜力.
- SR可以作为一个强大的工具来发现确切的解决方案,增强物理洞察力,并可能解决以前未解决的问题.
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