TorchSISSO：基于PyTorch的确定独立性筛选与稀疏化算子实现，用于高效且可解释的模型发现
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
AI总结：
针对原始SISSO符号回归算法FORTRAN实现性能低效、适配现代计算环境困难的问题，研究提出基于PyTorch的原生Python实现TorchSISSO，依托GPU加速实现性能提升与计算提速，拓展科学应用场景。
摘要：
符号回归（SR）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，能够为复杂数据提供可解释的紧凑表示。与传统回归方法不同，SR会逐步探索复杂度更高的特征空间，即便在小数据集上也能发掘出泛化能力良好的简洁模型。在各类SR算法中，确定独立性筛选与稀疏化算子（SISSO）已被证实在自然科学领域尤为有效，既能够重新发现基础物理定律，也能为材料性质建模挖掘新的可解释方程。然而，其基于FORTRAN的实现存在性能低效问题，在现代计算环境中适配难度大，限制了该方法的广泛应用。本研究提出了TorchSISSO，这是一款基于PyTorch框架构建的原生Python实现工具。TorchSISSO依托GPU加速能力，具备易集成、可扩展的特性，相比原始版本实现了显著的速度提升与精度优化。我们通过实验证明，TorchSISSO在一系列任务上的表现与原始SISSO相当甚至更优，同时大幅缩短了计算时间，提升了方法的可及性，可服务于更广泛的科学应用场景。
英文摘要：
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.
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