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arXiv Machine Learning By Yingjie Zhao, Zhiping Xu

Physics-Transfer Learning: A Physics-Constrained Theory for Zero-Shot Generalization Along the Ladder of Complexity

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Physics-Transfer Learning (PT) is a physics-constrained learning theory that enables zero-shot generalization across domains of increasing complexity. PT builds chains of anchor domains (CADs) linking low-cost source domains to complex targets and introduces computable feasibility and reliability indicators to assess transfer before target labels are available. The authors derive an upper bound on target risk under physical-consistency and stable-representation assumptions, and demonstrate PT’s effectiveness on quantum excited-state energy prediction and classical dynamical-transition prediction, achieving accurate high-fidelity predictions across substantial distribution shifts.

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