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Channel modeling is fundamental in advancing wireless systems and has thusattracted considerable research focus. Recent trends have seen a growingreliance on data-driven techniques to facilitate the modeling process and yieldaccurate channel predictions. In this work, we first provide a concise overviewof data-driven channel modeling methods, highlighting their limitations.Subsequently, we introduce the concept and advantages of physics-informedneural network (PINN)-based modeling and a summary of recent contributions inthis area. Our findings demonstrate that PINN-based approaches in channelmodeling exhibit promising attributes such as generalizability,interpretability, and robustness. We offer a comprehensive architecture forPINN methodology, designed to inform and inspire future model development. Acase-study of our recent work on precise indoor channel prediction withsemantic segmentation and deep learning is presented. The study concludes byaddressing the challenges faced and suggesting potential research directions inthis field.

Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges