Transformer-Based Model for Plasma Emission Tomography in Tokamak Divertor
A new study proposes Delta-InvFormer, a deep neural network that uses visible-light camera footage to predict the two-dimensional spatial distribution of light intensity in a tokamak divertor, aiding nuclear fusion research. The model employs a differential Transformer architecture with spatial and temporal self-attention to filter noise and capture plasma dynamics from consecutive video frames. This approach aims to provide a surrogate for neutral particle emission tomography, supporting future fusion experiments. The research is published on arXiv as a cross-type announcement.
Key facts
- Delta-InvFormer is a Transformer-based backbone network for plasma observation.
- It uses visible-light cameras to analyze spatio-temporal motion of plasma.
- The model predicts 2D light intensity distribution in the tokamak divertor.
- Spatial and temporal differential self-attention reduces noise interference.
- The input is consecutive video frames to capture plasma dynamics.
- The paper is published on arXiv with ID 2607.22704.
- The research targets nuclear fusion energy applications.
- The model serves as a surrogate for neutral particle emission tomography.
Entities
Institutions
- arXiv