Physics-informed deep learning for stochastic particle dynamics estimation.

ABSTRACT: Single-particle tracking has enabled quantitative studies of complex systems, providing nanometer localization precision and millisecond temporal resolution in heterogeneous environments. However, at micro- or nanometer scales, probe dynamics become inherently stochastic due to Brownian motion and complex interactions, leading to varied diffusion behaviors. Typically, analysis of such trajectory data involves certain moving-window operation and assumes the existence of some pseudo-steady states, particularly when evaluating predefined parameters or specific types of diffusion modes. Here, we introduce the stochastic particle-informed neural network (SPINN), a physics-informed deep learning framework that integrates stochastic differential equations to model and infer particle diffusion dyn

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11892597 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature