Inside a fusion reactor, plasma rages — a gas heated to millions of degrees. There are no direct windows, and cameras and sensors quickly fail. Engineers see only fragments of information: a few sensors at scattered points. It's like trying to grasp a symphony from the sound of three microphones in a vast hall.

The new machine learning model takes these sparse signals and reconstructs the full picture — all fields, temperatures, and densities at once. First, it compresses the measurement history over time. Think of a translator listening to speech and capturing the meaning, not individual words. Then, from this compressed representation, the model unfolds a detailed three-dimensional map of the plasma.

Tests on high-fidelity simulation data showed that signals from simple density sensors can recover several plasma characteristics at once. This isn't just filling in a picture — the model captures physical relationships between quantities. Such an approach could become the foundation for digital twins of the reactor. They provide real-time insights into what's happening inside and help keep the plasma stable.