Modeling Nuclear Fusion Reaction Occurrence with Advanced Deep Learning Techniques: Insights from LIME and SMOTE

Nuclear fusion, the process that powers the stars, presents an immense opportunity for clean and virtually limitless energy. However, achieving controlled nuclear fusion on Earth remains a significant scientific and engineering challenge due to the complex and nonlinear dynamics involved. Traditional modeling approaches often struggle to accurately capture these intricacies, hindering advancements in fusion reactor design and operation. This paper addresses these challenges by employing advanced deep learning techniques to enhance the prediction of nuclear fusion reaction occurrences. We implemented various deep learning models, including DNN, “Simple RNN,” LSTM, BiLSTM, GRU, and BiGRU, to forecast nuclear fusion reactions. The dataset, characterized by a significant class imbalance, was effectively balanced using SMOTE, ensuring that our models could accurately predict the minority class. Among the tested models, the BiLSTM model achieved the highest performance, with an impressive accuracy of 99% across all evaluation metrics. Additionally, we employed LIME, a XAI tool to provide insights into the feature importance, enhancing the transparency and interpretability of our models. The combination of SMOTE and LIME proved crucial in optimizing model performance and understanding feature contributions, making this approach a valuable tool for nuclear fusion prediction tasks.