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

TL;DR

This paper introduces a data-driven Mori-Zwanzig model to accurately simulate Lagrangian particle trajectories in turbulence, capturing both short-term dynamics and long-term statistical behavior efficiently.

Contribution

It develops a novel machine learning-based surrogate model grounded in the Mori-Zwanzig formalism for turbulent Lagrangian dynamics, enabling accurate short- and long-term predictions.

Findings

Model achieves point-wise short-time accuracy

Long-term statistical properties are reliably recovered

Enables efficient simulation of turbulent particle trajectories

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 approach is based on the Mori-Zwanzig formalism, which prescribes a mathematical decomposition of the full dynamical system into resolved dynamics that depend on the current state and the past history of a reduced set of observables, and the unresolved orthogonal dynamics due to unresolved degrees of freedom of the initial state. We show how by training this reduced order model on a point-wise error metric on short time-prediction, we are able to correctly learn the dynamics of Lagrangian turbulence, such that also the long-time statistical behavior is stably recovered at test time. This opens up a range of new applications, for example, for the control of active Lagrangian agents in turbulence.