Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
arXiv:2606.31574v1 Announce Type: cross Abstract: Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventional numerical methods, such as the Finite Element Method (FEM), are computationally expensive and therefore unsuitable for real-time applications. Therefore, a fast and generalizable method is required for real-time reconstruction of the divertor temperature field and subsequent real-time control. To address the above issue, we propose a Physics-aware Neural Operator
The increasing sophistication of AI models, particularly neural operators, allows for breakthroughs in complex scientific simulations that were previously computationally intractable in real-time.
Accurate real-time divertor temperature control is critical for safe and efficient operation of fusion reactors, advancing practical fusion energy towards commercial viability.
This advancement changes the paradigm for managing extreme conditions in fusion devices, moving from slow numerical simulations to rapid, AI-driven predictive control.
- · Fusion energy research
- · AI/ML in scientific computing
- · Energy sector
- · Traditional numerical methods (in real-time applications)
- · Less efficient fusion designs
Real-time, physics-aware AI models enhance the operational efficiency and safety of fusion devices.
Accelerated development and deployment of commercial fusion power plants become more feasible.
Widespread fusion energy could fundamentally transform global energy markets and reduce reliance on fossil fuels, with geopolitical implications.
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