Particle Trajectory

Particle trajectory analysis focuses on understanding and predicting the movement of particles, crucial across diverse fields from fluid dynamics to high-energy physics. Current research emphasizes leveraging machine learning, particularly deep neural networks (including convolutional and graph neural networks) and symbolic regression, to analyze complex trajectories, extract meaningful features (like vortex boundaries), and even infer underlying physical laws from experimental data. These advancements improve data analysis speed and accuracy in various applications, ranging from optimizing microfluidic devices to enhancing particle detection in high-energy physics experiments.