Analysis of Prediction and Control Strategies for Condensed Matter Physical Phase Transitions Based on Deep Learning
This paper describes the prediction and control technology of condensed matter physical phase transition based on deep learning.By analyzing and comparing the limitations of traditional methods,it points out that deep neural networks can effectively obtain the laws of various complex phase transition processes and achieve precise control of the processes.It constructs a unified prediction and control framework,designs an efficient modular network architecture,and innovatively introduces reinforcement learning algorithms.The validation on large-scale material phase transition data shows that the designed deep learning method can successfully predict the occurrence states of multiple types of phase transitions and accurately drive the phase transition process to the desired state through robust and efficient strategies.
deep learningphase transition predictionprocess controlreinforcement learning
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