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arXiv 2603.09855physics.plasm-ph
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稀疏识别有效微粒相互作用势在等离子体中的应用

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

Auburn University(奥本大学);Baylor University(贝勒大学)

Zachary Brooks Howe, Lorin Swint Matthews, Truell Hyde, Luca Guazzotto, Evdokiya Kostadinova

AI总结:

本文提出利用SINDy方法从模拟数据中稀疏识别微粒相互作用势,用于预测等离子体相变和结构形成。

摘要:

识别粒子相互作用势是等离子体、胶体和智能材料中的关键任务,有助于表征结构形成并预测相变。随着机器学习方法的发展,该相互作用可以从粒子位置数据中提取,从而得到通用表达式,适用于不同系统。稀疏回归等方法旨在提供可解释的模型,避免因过拟合导致的不必要的复杂性。本文展示了使用稀疏非线性动力学识别(SINDy)方法结合弱公式,从两个尘粒在Yukawa(屏蔽库仑)势下的简单模拟数据中学习运动方程。讨论了这些方法在实验等离子体数据中的应用,特别是模拟数据和玻璃箱实验在射频放电重力环境和直流放电微重力环境中的应用,如Plasmakristall-4(PK-4)实验。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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