DiffPhys: Differential Physics Augmentations for Enhanced Representations

Foundation Models (FMs) have revolutionized representation learning in IoT sensing applications. However, these models face a critical limitation: while their generalized representations excel at detection despite environmental distortions, they struggle to differentiate between fine-grained variations in these distortions—a capability essential for many IoT tasks like proximity assessment and dynamic tracking. Traditional augmentation approaches exacerbate this problem by focusing on invariance to these distortions, teaching models to ignore rather than distinguish meaningful environmental variations. To address these limitations, we introduce DiffPhys, a novel framework that fundamentally shifts how models learn from augmentations. DiffPhys incorporates two key innovations: (i) progressive physics-guided augmentations modeling environmental effects at varying intensities, and (ii) an ordinal consistency constraint structuring the embedding space to preserve physical relationships. To support this framework, we implement differentiable physics-guided augmentations that model progressive environmental effects like attenuation, Doppler shifts, and scattering. DiffPhys is designed as a pluggable module that seamlessly integrates into existing IoT-driven ML pipelines without requiring architectural changes to the underlying models. Evaluations across vehicle classification, speed estimation, and distance tracking tasks show that DiffPhys can enhance models to achieve up to 9% improvement in environmental differentiation tasks compared to standard versions.