PRIMS: Physics-guided Representation for Fluid Identification in Multimodal Sensing
Accurate on-device fluid identification is essential for microfluidic applications, yet maintaining reliability under varying flow, pressure, and temperature remains a key challenge. Existing learning-based methods often treat sensor signals as domain-agnostic features, neglecting the underlying physical relationships that govern fluid behavior, thereby limiting generalization and interpretability. To address this, we propose PRIMS, a physics-aware multimodal Transformer that integrates physical knowledge into representation learning and attention mechanisms through three dedicated modules: (1) Physics-based Token Vectorization transforms raw Coriolis and pressure sensor signals into phys...
| Authors | Hai-Long Nguyen, Trung Thanh Nguyen, Lars Holm et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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