PRIMS: Physics-guided Representation for Fluid Identification in Multimodal Sensing

On-device fluid identification for microfluidic applications is challenging under varying flow, pressure, and temperature, and existing learning methods ignore underlying physics. PRIMS addresses this with a physics-aware multimodal Transformer combining physics-based sensor token vectorization, a viscosity-aware component synthesizer, and physics-guided attention fusion, embedding fluid mechanics directly into the architecture. On a five-fluid benchmark, PRIMS achieved 98.92% F1-score with just 0.46 million parameters, a 14x reduction versus prior Transformers, and stayed robust under unseen conditions. Applications include lab-on-chip diagnostics, industrial fluid monitoring, and lightweight, interpretable edge-deployed sensing systems for microfluidics. Authors: Hai-Long Nguyen, Trung Thanh Nguyen, Lars Holm, Dennis Alveringh, Duc Viet Le Paper: https://arxiv.org/abs/2607.22422v1