Junhua Gong1, Yujie Chen2,*, Bo Yu2, Bin Chen1
The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.012531
Abstract Traditional interface reconstruction methods often rely on numerical approaches, which can be inefficient when dealing with large bubbles, requiring extensive computational resources. To address this issue, we propose a novel model based on convolutional neural networks aimed at rapidly and accurately predicting the equations governing circular bubbles. This model takes the volume fraction of the main-phase fluid surrounding each computational grid cell as input variables and is capable of precisely forecasting the coordinates and radii of bubbles. To further enhance model performance, we employ the Optuna hyperparameter optimizer to fine-tune the model's parameters. Upon training More >
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