[2507.16058] Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows

Physics > Fluid Dynamics arXiv:2507.16058 (physics) [Submitted on 21 Jul 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows Authors:Xander de Wit, Alessandro Gabbana, Michael Woodward, Yen Ting Lin, Federico Toschi, Daniel Livescu View a PDF of the paper titled Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows, by Xander de Wit and 5 other authors View PDF HTML (experimental) Abstract:The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly non-trivial statistical behavior, motivating the development of surrogate models that can reproduce these trajectories without incurring the high computational cost of direct numerical simulations of the full Eulerian field. This task is particularly challenging because reduced-order models typically lack access to the full set of interactions with the underlying turbulent field. Novel data-driven machine learning techniques can be powerful in capturing and reproducing complex statistics of the reduced-order/surrogate dynamics. In this work, we show how one can learn a surrogate dynamical system that is able to evolve a turbulent Lagrangian trajectory in a way that is point-wise accurate for short-time predictions (with respect to Kolmogorov time) and stable and statistically accurate at long times. This ap...