一种基于物理的神经框架用于从动态激光散斑测量流变学
A Physics-Guided Neural Framework for Rheology Measurement from Dynamical Laser Speckles
AI总结：
本文提出了一种基于物理的深度学习框架，通过分析动态激光散斑数据，实现对混浊介质中流变学性质的高效测量。
摘要：
在生物医学和材料科学领域，关键突破越来越依赖于快速、非接触的粘弹性表征方法。激光散斑流变学（LSR）被定位为满足这一需求，有效绕过了传统机械流变仪固有的速度和侵入性瓶颈。然而，其在混浊流体中的应用受到多重散射的严重限制，其中标准物理反演严重依赖于精确的、样品特定的光学传输参数，这些参数在原位测量时难以获得。为克服这一障碍，我们提出了一种基于物理的深度学习框架，该框架从强度自相关g2(t)和散斑强度直方图统计信息推断出Maxwell松弛谱。所得的谱随后通过Maxwell正向模型传播，以在物理一致性约束下预测G'和G''。定量上，该框架的RMSElog低至0.009，相对于参考值并能推广到以前未见过的散射条件，保持了物理合理的时间依赖性和G' - G''相位行为。它减少了对难以原位确定的光学传输参数的依赖，并返回了一个可解释的广义Maxwell松弛谱，提高了LSR在混浊介质中的实用性。
英文摘要：
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.
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