This paper presents the Physics-Informed Impact Identification (Phy-ID) framework. It addresses the challenges of reconstructing impact parameters from passive sensor signals. Phy-ID integrates physical knowledge into machine learning across three complementary levels: how data representation is defined, how the model is built, and how optimisation is guided. Each level is described conceptually and illustrated with documented examples from previous work of the authors and the literature. Examples cover both established strategies and new directions yet to be applied to impact identification. By aligning model design with prior knowledge of composite structures under impact, Phy-ID provides a structured, scalable modelling approach. It targets improved robustness, interpretability, and generalisation in conditions of partial physical knowledge and limited experimental data, which are typical in real-world SHM.