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A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
Physics-informed machine learning (PIML) integrates mechanistic knowledge, typically through partial differential equations (PDEs), into data-driven models. Despite strong empirical performance, its statistical generalisation properties remain poorly understood, especially for regression with unboun
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