论文标题
从灰尘等离子体中的嘈杂动力学中提取力
Extracting Forces from Noisy Dynamics in Dusty Plasmas
论文作者
论文摘要
从嘈杂数据中提取环境力是在复杂物理系统中的一项普遍但又具有挑战性的任务。机器学习代表了解决此问题的强大方法，但主要在具有已知参数的模拟数据上测试。在这里，我们使用监督的机器学习来提取作用于氩等离子体悬浮的微米大小的电荷颗粒上的静电，流体动力和随机力。该模型使用超过100个动力学和统计特征对模拟粒子轨迹进行了训练，该模型比传统方法预测具有50 \％精度的系统参数，并提供了粒子电荷和Debye长度的非接触式测量。
Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning represents a robust approach to this problem, yet is mostly tested on simulated data with known parameters. Here we use supervised machine learning to extract the electrostatic, hydrodynamic, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma. Trained on simulated particle trajectories using more than 100 dynamical and statistical features, the model predicts system parameters with 50\% better accuracy than conventional methods, and provides non-contact measurements of the particle charge and Debye length.
