Deep Learning-Based Phase Transition Prediction Model in Nonequilibrium Thermodynamic Systems

Nonequilibrium thermodynamic systems represent a class of complex systems widely observed in both natural phenomena and industrial applications. Their phase transitions constitute a critical research topic in physics, chemistry, and materials science due to their intricate dynamic behaviors and the influence of multiple factors. Conventional thermodynamic theories and numerical simulation methods encounter significant challenges in predicting phase transitions within nonequilibrium systems, including excessive computational demands, inefficiencies, and strong dependencies on initial and boundary conditions. Recently, the integration of deep learning techniques, particularly the Dynamic Graph Neural Network (DGNN), has provided new avenues for addressing these challenges. In this study, the phase transition problem in nonequilibrium thermodynamic systems was systematically examined, and key influencing factors were analyzed. A predictive approach based on DGNN was proposed, leveraging both temporal and structural features of the system to achieve efficient and accurate phase transition forecasting. By innovatively applying DGNN, the accuracy and efficiency of phase transition prediction were significantly enhanced, offering novel tools and methodologies for studying complex systems.