Extracting Dynamical laws in Dusty Plasmas using Machine Learning

Machine learning is concomitant with highly complex systems where billions of data readily available. For individual experimental physics labs, such data is often expensive or time-consuming. Instead, simulated data with known underlying governing equations are used to train models. Here we provide a tractable experimental system with complex dynamics and copious amounts of data. By tracking the 3D motion of hundreds of levitated microparticles in a “dusty” plasma, we can tease apart the known and unknown forces and “learn” their underlying physics. Dusty plasmas are ubiquitous in the space and industry and exhibit a rich spectrum of forces (electrostatic, hydrodynamic, ion wake, drag, and stochastic noise). We can reveal many of these forces by analyzing the “Brownian” motion of the particles using machine learning trained on simulated data. In simulations, the forces are linearized and features extracted from trajectories using conventional methods (Fourier transformation, Bayesian Inference, and mutual information, etc.) are then fed to machine learning models. We can predict linear coefficients with a two-fold precision over conventional methods, and uncover new surprises such as the heavy-tailness of stochastic noise, and vorticity in the background ion flow.