🤖 AI Summary
The opacity of deep neural networks severely limits the applicability of machine learning in physical sciences, where interpretability, derivability, and pattern recognition are equally essential. Method: We propose Symbolic Machine Learning (Symbolic ML) as a complementary paradigm to numerical ML, systematically integrating symbolic regression, program induction, logical reasoning, and domain-knowledge embedding to transcend the black-box paradigm. Contribution/Results: We establish, for the first time, the epistemologically equal and synergistic roles of symbolic and numerical ML in physics; clarify fundamental methodological distinctions between ML and traditional physical reasoning; and formulate a new physics-aware AI paradigm—characterized by interpretability, verifiability, and generalizability. This framework provides both theoretical foundations and practical guidelines for accelerating scientific discovery through intelligible, principled, and reproducible AI.
📝 Abstract
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.