Physical information-enhanced graph neural network for predicting phase separation
Yaqiang 亚强 Zhang 张,
Xuwen 煦文 Wang 王,
Yanan 雅楠 Wang 王
et al.
Abstract:Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here, we present a physical information-enhanced graph neural network (PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we desi… Show more
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