Sparse identification of effective microparticle interaction potential in dusty plasma from simulation data

Abstract

Identification of the particle interaction potential is a challenging and important task in dusty plasma, colloids, and smart materials as it allows the characterization of structure formation and helps predict phase transitions. With the advent of machine learning methods, this interaction can be extracted from particle position data, leading to a generalizable expression which is applicable in different systems. Methods such as sparse regression aim to provide a physically interpretable model that can generalize well, while avoiding unnecessary complexity due to overfitting. In this work, we present the use of the Sparse Identification of Nonlinear Dynamics (SINDy) with the weak formulation to learn equations of motion for noisy data from simple simulations of two dust particles interacting with a Yukawa (shielded Coulomb) potential. The application of these methods to experimental dusty plasma data is discussed, particularly in the case of simulation data and glass box experiments in RF discharge gravity environments and DC discharge microgravity environments, such as the Plasmakristall-4 (PK-4) experiment.

11 pages, 4 figures. This work has been submitted to the Physics of Plasmas for possible publication

References in corpus (15)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• PySINDy: A comprehensive Python package for robust sparse system identification
• Statistical mechanics for non-reciprocal forces
• Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression
• Crystallization dynamics of a single layer complex plasma
• Data-driven discovery of reduced plasma physics models from fully-kinetic simulations
• Dust charging in dynamic ion wakes
• Learning Mean-Field Equations from Particle Data Using WSINDy
• Three-dimensional structure of a string-fluid complex plasma
• Effect of ionization waves on dust chain formation in a DC discharge
• Influence of Temporal Variations in Plasma Conditions on the Electric Potential Near Self-Organized Dust Chains
• Microparticle cloud imaging and tracking for data-driven plasma science
• Extracting Forces from Noisy Dynamics in Dusty Plasmas
• Glow and dust in plasma boundaries
• Structural States of Filamentary Microgravity Dusty Plasma