DISCOVER: A Physics-Informed, GPU-Accelerated Symbolic Regression Framework
Humboldt-Universität zu Berlin（柏林洪堡大学）；Ulm University（乌尔姆大学）；Helmholtz Institute Ulm (HIU)（赫尔霍兹乌尔姆研究所）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Udaykumar Gajera, Mohsen Sotoudeh, Kanchan Sarkar, Axel Groß
备注：
main paper with only 7 pages including 1 figure along with supplementary materials with mathematics guide
符号回归（SR）能够从实验和模拟数据中发现可解释的数学关系。这些关系通常被称为描述符，定义为一种基本材料属性，直接与材料的期望或非期望功能属性相关联。尽管已建立的方法如Sure Independence Screening和Sparsifying Operator（SISSO）已成功在大规模特征空间中识别出低维描述符，但许多现有的SR工具与现代Python工作流程整合不佳，对符号搜索空间的控制有限，或在大规模研究的计算需求上遇到困难。本文介绍了DISCOVER（Data-Informed Symbolic Combination of Operators for Variable Equation Regression），一个开源的符号回归包，旨在通过模块化、以物理为基础的设计来解决这些挑战。DISCOVER允许用户利用领域知识引导符号搜索，明确约束特征空间，并利用可选的GPU加速来提高数据密集型工作流程的计算效率，从而实现可重复和可扩展的SR工作流程。该软件旨在用于计算物理、计算化学和材料科学领域，其中可解释性、物理一致性和执行时间尤为重要，并通过强调发现具有物理意义的模型来补充通用的SR框架。
英文摘要：
Symbolic Regression (SR) enables the discovery of interpretable mathematical relationships from experimental and simulation data. These relationships are often coined descriptors which are defined as a fundamental materials property that is directly correlated to a desired or undesired functional property of the material. Although established approaches such as Sure Independence Screening and Sparsifying Operator (SISSO) have successfully identified low-dimensional descriptors within large feature spaces many existing SR tools integrate poorly with modern Python workflows, offer limited control over the symbolic search space, or struggle with the computational demands of large-scale studies. This paper introduces DISCOVER (Data-Informed Symbolic Combination of Operators for Variable Equation Regression), an open-source symbolic regression package developed to address these challenges through a modular, physics-motivated design. DISCOVER allows users to guide the symbolic search using domain knowledge, constrain the feature space explicitly, and take advantage of optional GPU acceleration to improve computational efficiency in data-intensive workflows, enabling reproducible and scalable SR workflows. The software is intended for applications in computational physics, computational chemistry, and materials science, where interpretability, physical consistency, and execution time are especially important, and it complements general-purpose SR frameworks by emphasizing the discovery of physically meaningful models.
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University College Cork（科克大学学院）；Institute for the Conservation of Cultural Heritage, School of Cultural Heritage and Information Management, Shanghai University（上海大学文化遗产与信息管理学院文化遗产保护研究所）
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College of Engineering, Peking University（北京大学工学院）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）
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Institute of Theoretical and Computational Physics（理论与计算物理研究所）；University of Crete（克里特大学）；National University of Science and Technology MISiS（国立科技大学MISiS）
