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

2026-07-24

AI Summary

This work addresses the poor generalization and lack of interpretability in existing on-chip fluid identification methods under dynamic flow velocity, pressure, and temperature conditions, which stem from their neglect of underlying physical laws governing fluid behavior. To overcome these limitations, the authors propose PRIMS—a physics-informed multimodal Transformer that innovatively embeds fluid dynamics principles into its architecture through three key components: physics-aware token vectorization, a physics-based constituent synthesizer, and physics-guided fusion. By leveraging attention mechanisms to model viscosity-dependent dependencies among flow velocity, pressure, and density, PRIMS achieves an average F1 score of 98.92% across five fluid types with only 0.46M parameters—14× fewer than existing Transformers—while demonstrating exceptional generalization and environment-invariant representation capabilities under unseen temperature ranges and flow velocities.

Abstract

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 physically meaningful token embeddings; (2) Physical Component Synthesizer models viscosity-related dependencies among flow, pressure, and density; and (3) Physics-guided Fusion captures cross-physical correlations through attention-based integration. By embedding these physics-based relationships directly into the model architecture, PRIMS bridges analytical fluid mechanics and deep learning, enabling interpretable, data-efficient, and resilient fluid classification. Evaluations on a five-fluid benchmark under dynamic flow, pressure, and temperature conditions show that PRIMS achieves 98.92% average F1-score with only 0.46 million parameters, a 14 times reduction compared to state-of-the-art Transformer-based methods. PRIMS also consistently outperforms prior SOTA models under out-of-distribution shifts to unseen temperature ranges and unseen flow-rate ranges, indicating strong robustness to operating conditions not observed during training. These findings suggest that designing architectures that explicitly mirror governing physical relationships can make them learn transferable, environment-independent representations, improving real-world reliability for microfluidic sensing.

Problem

• fluid identification
• microfluidics
• multimodal sensing
• physical relationships
• generalization

Innovation

• physics-guided learning
• multimodal sensing
• fluid identification
• Transformer architecture
• physical representation