物理科学中的人工智能:象征回归趋势和前景
Dimitrios Angelis1, Filippos Sofos1, Theodoros E Karakasidis1
1Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia, 35100 Greece.
概括
符号回归 (SR) 是一种强大的机器学习技术,可以直接从数据中发现分析方程,从而消除了对先前系统知识的需求. 本次审查涵盖了SR的SR.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算科学 计算科学
背景情况:
- 符号回归 (SR) 是一种数据驱动的机器学习方法.
- 它利用遗传编程原理,并整合了各种科学技术.
- 在没有先前的系统知识的情况下,SR可以发现复杂,可概括和可解释的关系.
研究的目的:
- 提供对象征回归当前最先进技术的全面审查.
- 详细介绍SR方法的技术和物理特性.
- 探索现有的编程技术,应用领域和未来的研究方向.
主要方法:
- 审查关于符号回归的现有文献.
- 分析SR的技术和物理属性.
- 研究各种编程方法及其应用.
主要成果:
- SR可以直接从数据中生成分析方程.
- 它减少了对事先存在的领域专业知识的必要性.
- 在科学,技术,经济和社会领域,SR识别了深刻和模两可的关系.
结论:
- 符号回归为科学发现提供了一种强大的,无知识的方法.
- 它能够从数据中推导出可解释的方程,这使得它具有广泛的应用.
- 该领域对未来的进步和更广泛的采用具有重大前景.
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