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

TL;DR

This paper demonstrates a method using sparse regression to identify the effective interaction potential between particles in dusty plasma from simulation data, aiding understanding of structure formation and phase transitions.

Contribution

It introduces the application of SINDy with weak formulation to learn particle interaction equations from noisy simulation data in dusty plasma systems.

Findings

Successfully identified Yukawa potential parameters from simulation data.

Showed potential applicability to experimental dusty plasma data.

Provided a physically interpretable model for particle interactions.

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.