物理科学中的人工智能：符号回归趋势与展望。

Angelis Dimitrios, Sofos Filippos, Karakasidis Theodoros E

Condensed Matter Physics Laboratory, Department of Physics, University of Thessaly, Lamia, 35100 Greece.

Arch Comput Methods Eng. 2023 Apr 19:1-21. doi: 10.1007/s11831-023-09922-z.

UNLABELLED

Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11831-023-09922-z.

未标注

符号回归（SR）是一种基于遗传编程原理的机器学习回归方法，它整合了来自不同科学领域的技术和过程，能够仅从数据中提供分析方程。这一显著特性减少了纳入有关被研究系统的先验知识的需求。SR可以发现深刻的关系，并阐明那些可推广、适用、可解释且涵盖大多数科学、技术、经济和社会原理的模糊关系。在本综述中，记录了当前的技术水平，介绍了SR的技术和物理特性，研究了可用的编程技术，探索了应用领域，并讨论了未来展望。