Research map / Probabilistic and scientific modeling / Molecules and proteins
Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy
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Position paper that identifies six ML research challenges in magnetic confinement fusion (disruption prediction, simulation and dynamics modeling, partial observability, control, optimal experiment design, materials design) and discusses fusion data ecosystem issues.
Problem
Magnetic confinement fusion faces technical hurdles, and the ML community has little structured guidance on which fusion problems are ripe for ML contributions.
Approach
Surveys six fusion problems that are especially suited to ML, giving for each background, prior work, future model features and challenges, and frames the opportunities for the ML audience rather than fusion practitioners.
Limitations
- Covers only magnetic (tokamak-focused) fusion, not inertial or other confinement approaches
- No experiments; claims about opportunities are qualitative
Experimental setup
Builds on: physics-informed neural networks machine learning interatomic potentials Bayesian optimization
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