A Physics-Guided Neural Framework for Rheology Measurement from Dynamical Laser Speckles
Critical breakthroughs in the area of biomedicine and materials science increasingly depend on rapid, non-contact methods for viscoelastic characterization. Laser Speckle Rheology (LSR) is positioned to meet this demand, effectively circumventing the speed and invasiveness bottlenecks inherent to traditional mechanical rheometer. However, its application in turbid fluids is severely constrained by multiple scattering, where standard physical inversions rely heavily on precise, sample-specific optical transport parameters that are difficult to measure in situ. To overcome this barrier, we propose a physics-guided deep learning framework that infers a Maxwell relaxation spectrum from the intensity autocorrelation g2(t) and speckle-intensity histogram statistics. The resulting spectrum is then propagated through a Maxwell forward model to predict G'and G'' under physics-consistency constraints. Quantitatively, the framework achieves RMSElog as low as 0.009 against reference and generalizes to previously unseen scattering conditions, preserving physically plausible frequency dependence and G'- G'' phase behavior. It reduces reliance on optical transport parameters that are hard to determine in situ and returns an interpretable generalized Maxwell relaxation spectrum, improving the practicality of LSR in turbid media.