Reinforcement learning (RL) is a unique learning
paradigm inspired by the behaviour of animals and humans to learn to solve tasks autonomously. Learning occurs
through interactions with an environment, exploring, and
evaluating strategies under various conditions. RL excels in
complex environments, can handle delayed consequences,
and is able to learn solely from experience without...
Safety-critical systems — particle accelerators, reactors, enrichment cascades — occupy a category apart. Failure is not a degraded user experience but a physical hazard, and the properties that define these systems (hard real-time constraints, formally verified interlocks, regulatory traceability, and a low tolerance for opacity) are precisely the properties that make standard...
IGNITE is a multi-modal world model for full discharge simulation, trained on a large-scale dataset of DIII-D experimental data. This world model has been developed as part of the DOE Fusion Science & Technology Roadmap [1]. Inspired by the success of scientific world models for weather and climate forecasting [2,3,4], IGNITE is an end-to-end AI model that ingests heterogeneous diagnostic...
