Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion

TL;DR

This paper introduces a deep learning approach using multimodal fusion and Transformer attention mechanisms to improve Q-distribution prediction accuracy in controlled nuclear fusion, demonstrating significant error reduction.

Contribution

It presents a novel multimodal fusion method combining 2D images and 1D data with Transformer-based feature extraction for enhanced Q-distribution prediction.

Findings

Significant reduction in prediction errors.

Effective integration of multimodal data.

Validation through extensive experiments.

Abstract

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.