Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion
This addresses prediction challenges in nuclear fusion, an incremental improvement using existing methods on new data.
The paper tackled Q-distribution prediction in controlled nuclear fusion by integrating 2D line image data with 1D data using multimodal fusion and Transformer attention mechanisms, significantly reducing prediction errors.
Q-distribution prediction is a crucial research direction in controlled nuclear fusion, with deep learning emerging as a key approach to solving prediction challenges. In this paper, we leverage deep learning techniques to tackle the complexities of Q-distribution prediction. Specifically, we explore multimodal fusion methods in computer vision, integrating 2D line image data with the original 1D data to form a bimodal input. Additionally, we employ the Transformer's attention mechanism for feature extraction and the interactive fusion of bimodal information. Extensive experiments validate the effectiveness of our approach, significantly reducing prediction errors in Q-distribution.