Spatiotemporal Evolution Prediction of Complex Plasma Self‐Organization Effects Based on PredRNN

This study presents a machine‐learning‐based method to predict the spatiotemporal evolution of dust particle configurations in two‐dimensional dusty plasmas. Evolutionary datasets are generated via molecular dynamics simulations under the Yukawa potential approximation, and particle configurations are encoded as fixed‐resolution grayscale image sequences. A spatiotemporal recurrent neural network, PredRNN, is adopted to model the structural evolution of dust particles and conduct rolling predictions from a limited number of initial frames. Results demonstrate that PredRNN effectively captures the dynamical behavior of dust particles and yields predictions consistent with molecular dynamics simulations for key structural features, including cluster formation, coalescence, and long‐term evolution. Quantitative evaluations using MSE, SSIM, and LPIPS further validate the model's predictive performance at both structural and perceptual levels. The proposed framework accurately reproduces the self‐organized structural evolution of dust systems and offers a data‐driven perspective for understanding collective self‐organization dynamics in dusty plasmas. It also provides an efficient computational approach for long‐timescale extrapolation.