Pilot-Wave Dynamics: Using Dynamic Mode Decomposition to characterize Bifurcations, Routes to Chaos and Emergent Statistics
J. Nathan Kutz, Andre Nachbin, Peter J. Baddoo, John W. M. Bush

TL;DR
This paper uses Dynamic Mode Decomposition to analyze the bifurcations and chaos in a pilot-wave hydrodynamic system, revealing links to quantum mechanics and predicting particle statistics from wave dynamics.
Contribution
It introduces a data-driven DMD approach to characterize bifurcations and chaos in pilot-wave systems, connecting wave field evolution with droplet statistics.
Findings
Wave field undergoes Hopf bifurcations leading to chaos.
DMD provides a low-rank model of bifurcation structure.
Wave theory predicts particle statistics similar to quantum mechanics.
Abstract
We develop a data-driven characterization of the pilot-wave hydrodynamic system in which a bouncing droplet self-propels along the surface of a vibrating bath. We consider drop motion in a confined one-dimensional geometry, and apply the {\em Dynamic mode decomposition} (DMD) in order to characterize the evolution of the wave field as the bath's vibrational acceleration is increased progressively. DMD provides a regression framework for adaptively learning a best-fit linear dynamics model over snapshots of spatio-temporal data. The DMD characterization of the wave field yields a fresh perspective on the bouncing-droplet problem that forges valuable new links with the mathematical machinery of quantum mechanics. Moreover, it provides a low-rank characterization of the bifurcation structure of the pilot wave physics. Specifically, the analysis shows that as the vibrational acceleration is…
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Taxonomy
TopicsGaussian Processes and Bayesian Inference · Fluid Dynamics and Turbulent Flows · Lattice Boltzmann Simulation Studies
