arXiv:2511.04567v2 Announce Type: replace-cross Abstract: Constructing reduced models for turbulent transport is essential for accelerating profile predictions and enabling many-query tasks such as parameter exploration and design optimization. This work investigates machine-learning-driven reduced models for Electron Temperature Gradient (ETG) turbulence in the Wendelstein 7-X (W7-X) stellarator. We develop physics-guided scaling laws to predict the ETG heat flux at seven radial locations as functions of three key plasma parameters: the normalized electron temperature gradient ($\omega_{T_e}$
The increasing computational demands of fusion research and the maturation of AI/ML techniques for complex scientific problems are enabling new approaches to plasma modeling.
Advanced and more efficient modeling of electron-scale turbulence is critical for developing viable fusion energy, which represents a long-term solution to global energy needs.
Machine learning is being directly applied to accelerate and improve the accuracy of complex plasma physics simulations, potentially speeding up design and optimization for stellarators like W7-X.
- · Fusion energy research
- · AI/ML in scientific computing
- · Plasma physicists
- · Stellarator developers
- · Traditional high-fidelity simulation methods (relative cost/time inefficiency)
Faster iteration cycles for fusion reactor design and optimization may shorten the timeline to commercial fusion.
Success in fusion could significantly alleviate the future energy demands of advanced computing infrastructure and global power grids.
A future energy abundant world powered by fusion could profoundly reshape geopolitical power dynamics and mitigate climate change impacts.
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Read at arXiv cs.LG