A Perspective on Symbolic Machine Learning in Physical Sciences
This perspective highlights a need for interpretable ML in physics research, but it is incremental as it builds on existing ideas about symbolic methods.
The paper argues that symbolic machine learning should be developed and applied alongside numerical methods in physics to address the slow adoption of ML in physical sciences due to the uninterpretability of deep neural networks.
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.