Machine Learning Solutions
AI and ML are essential to match the complexity and speed of fusion. We've been working on ML-based solutions from the beginning of the company.
Focus Areas
Our machine learning engineers work closely with physicists and fusion software developers to build solutions that are grounded in proven science and either device-agnostic or easily transferable across devices. We also focus on methods relevant to new fusion devices that start without a historical dataset of discharges to train models on.
Plasma control with reinforcement learning
We develop and deploy RL-based controllers for plasma shape and position, trained inside the NSFsim simulation environment and validated directly on DIII-D — eliminating the need for equilibrium reconstruction and enabling high-speed control execution.
Plasma parameters reconstruction
Combining model-based and machine-learning methods for real-time plasma parameters reconstruction and uncertainty quantification — from neural network reconstruction of plasma boundaries to L-H mode classification using reactor-relevant ECE diagnostics data.
Surrogate models and physics acceleration
Physics-informed neural networks trained on integrated modeling ensembles to replace expensive physics codes with fast, accurate surrogates — enabling real-time inference for plasma control, rapid scenario screening, and uncertainty quantification at scales impractical with first-principles simulations.
Discharge scenario builder
Combining physics-based simulation with machine learning to automate discharge scenario development — learning from large ensembles of NSFsim runs to optimize plasma current ramp-up, heating schemes, and confinement targets while respecting hardware and stability constraints.
Case Studies
Our ML work spans reinforcement learning for plasma control, confinement state classification, and neural network reconstruction — all developed in close collaboration with physicists and validated on real devices such as DIII-D.
Controlling plasma temperature and safety factor with gyrotrons using reinforcement learning
Paper ↗Plasma confinement state classification via FPP relevant microwave diagnostics
Paper ↗Neural network reconstruction of the DIII-D tokamak plasma boundary using a reduced set of diagnostics
Paper ↗Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning
Paper ↗Robustness by design: Interface contracts for AI control in high-stakes physical systems
See the publications page to learn more about our research and work.
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