提出符号机器学习助力物理科学发现，弥补深度模型不可解释的短板。
A Perspective on Symbolic Machine Learning in Physical Sciences
- 用符号方法增强神经网络的可解释性，加速物理研究
- 强调符号与数值方法应并行发展，符合物理研究双重特性
- 适合关注模型可解释性与理论推导的物理学者
机器学习正快速渗透自然科学各领域，但在物理科学中的影响速度仍远不及非科学领域。这在一定程度上源于深度神经网络的不可解释性。符号机器学习作为数值机器学习的平等互补伙伴，能有效推动物理学中的科学发现。本文探讨了机器学习与科学方法的核心差异，强调必须同等重视并应用符号机器学习于物理问题，因其契合物理研究的双重本质——既需数据驱动，也需理论可解释。
原文摘要 · Abstract (English)
Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.
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