Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningPredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction
The prediction of nanoparticles (NPs) distribution is crucial for the diagnosis and treatment of tumors. Recent studies indicate that the heterogeneity of tumor microenvironment (TME) highly affects the distribution of N…
Image GenerationMedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis
Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing modalities at training and test time, and sample-specific variation in …
Medical DiagnosisMotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents. Such representation has several key advantages: first, our model learns a highly multimo…
Motion Forecastingmotion predictionPredictionSemantic-Space-Intervened Diffusive Alignment for Visual Classification
Cross-modal alignment is an effective approach to improving visual classification. Existing studies typically enforce a one-step mapping that uses deep neural networks to project the visual features to mimic the distribu…
Classificationcross-modal alignmentFusion framework and multimodality for the Laplacian approximation of Bayesian neural networks
This paper considers the problem of sequential fusion of predictions from neural networks (NN) and fusion of predictions from multiple NN. This fusion strategy increases the robustness, i.e., reduces the impact of one in…
image-classificationImage Classification