Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.

翻译：符号回归是一种强大的机器学习方法，它同时搜索代数模型的结构与参数，为复杂数据提供可解释且紧凑的表示。与传统回归方法不同，符号回归逐步探索复杂的特征空间，即使从小型数据集中也能发现泛化能力强的简单模型。在众多符号回归算法中，确定性独立筛选与稀疏化算子（SISSO）已在自然科学领域被证明特别有效，它有助于重新发现基本物理定律，并为材料特性建模发现新的可解释方程。然而，其性能效率不足以及基于FORTRAN的实现所带来的挑战（尤其是在现代计算环境中）限制了该算法的广泛采用。本研究介绍了TorchSISSO，一个基于PyTorch框架构建的原生Python实现。TorchSISSO利用GPU加速、易于集成和可扩展性，相比原始版本实现了显著的加速和精度提升。我们证明，在一系列任务中，TorchSISSO的性能达到或超越了原始SISSO，同时大幅减少了计算时间，并提高了其在更广泛科学应用中的可及性。