Dicovering the emergent nonlinear dynamics of acoustically levitated cube clusters

TL;DR

This paper introduces a data-driven approach to uncover the nonlinear dynamics of acoustically levitated cube clusters, revealing complex long-term behaviors and interactions between degrees of freedom.

Contribution

It develops a minimal nonlinear dynamical model from experimental data, capturing long-time behavior and nonlinear coupling in a levitated particle system.

Findings

Identified three distinct nonlinear attractors in cube cluster dynamics.

Extracted a minimal model explaining the stabilization of these attractors.

Confirmed the model's physics with finite-element simulations.

Abstract

The complex behavior of many natural and engineered systems emerges from the interaction of a small number of effective degrees of freedom. Discovering the physical basis of the interactions between these degrees of freedom directly from experimental observations has been a longstanding challenge, particularly with respect to predicting the long-time dynamics of dynamical systems with unknown equations of motion. Here, we introduce a data-driven approach that is able to produce a generative model for the long-time dynamical behavior of systems with a weakly attracting manifold. We apply this method to an experimental dynamical system with two degrees of freedom: acoustically levitated pairs of cube-shaped particles, which cluster by sharing a single edge. In the acoustic trap, the center-of-mass of the cube cluster oscillates vertically about the levitation plane, while also oscillating about their flexible hinge-like connection. Depending on their initial condition, the hinge dynamics evolve about three distinct nonlinear dynamical attractors persisting for hundreds of cycles. In order to capture the underlying physics, we develop a numerical fitting procedure and extract a minimal nonlinear dynamical model that captures both the long-time dynamics of the cluster as well as the convergence onto the dynamical steady state. This dynamical model uncovers the nonlinear, non-reciprocal coupling between the center-of-mass motion and the hinge degree of freedom that stabilizes the dynamical attractors, which we subsequently confirm by independent finite-element methods. Our results demonstrate a novel data-driven method for the discovery of nonlinear models with long-timescale stable predictions.