A team of US researchers has introduced a groundbreaking artificial intelligence (AI) system designed to protect fusion reactors from the intense heat of plasma.

The method, called HEAT-ML, was developed by Commonwealth Fusion Systems (CFS), the US Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory. It can rapidly pinpoint magnetic shadows—regions shielded from plasma’s searing heat—allowing engineers to prevent damage before it occurs.

Quickly locating these zones is critical to sustaining fusion operations, where plasma temperatures can soar beyond those found at the Sun’s core. According to the team, HEAT-ML could form the basis for future software that accelerates reactor design and enables real-time decision-making by dynamically adjusting plasma conditions.

Figure 1. Some of the Inner Surfaces Are Directly Exposed to the Plasma.

Tackling the Plasma Heat Challenge

Fusion—the reaction that powers stars—offers the promise of virtually limitless, carbon-free energy. However, one of the biggest hurdles is managing plasma, which inside a tokamak (a doughnut-shaped reactor that uses powerful magnets to confine plasma) can burn hotter than the Sun. Predicting where this heat will strike is essential to protecting plasma-facing components from melting or damage.

“Plasma-facing components might come into direct contact with plasma, which is extremely hot and capable of damaging these elements,” said Doménica Corona Rivera, associate research physicist at PPPL and lead author of the study. Figure 1 represents some of the inner surfaces are directly exposed to the plasma.

HEAT-ML is an AI-powered enhancement of the open-source Heat flux Engineering Analysis Toolkit (HEAT), which produces 3D “shadow masks” showing which reactor surfaces are shielded from plasma contact. The new AI was trained specifically for SPARC; a tokamak being built by CFS in Massachusetts that aims to demonstrate net energy gain by 2027. Initial testing targeted 15 tiles near SPARC’s exhaust system, an area expected to face extreme thermal loads.

Faster Simulations for Fusion’s Future

Traditionally, HEAT calculates shadow regions by tracing magnetic field lines from surfaces, a process that can take up to 30 minutes per simulation—or longer for complex geometries. HEAT-ML dramatically speeds this up by using a deep neural network trained on about 1,000 SPARC simulations. Once trained, it can generate accurate shadow masks in milliseconds, reducing computation times by several orders of magnitude.

“This research shows you can take an existing code and build an AI surrogate to deliver faster, useful results,” said Michael Churchill, head of digital engineering at PPPL and co-author of the study. “It also opens new possibilities for control strategies and scenario planning.”

By accelerating critical calculations, HEAT-ML could help pave the way toward making fusion energy a reliable, grid-ready power source.

References:

1. https://interestingengineering.com/science/new-ai-method-speeds-up-calculations-to-protect-fusion-reactors-from-plasma-heat

Cite this article:

Keerthana S (2025), New AI Approach Accelerates Calculations to Shield Fusion Reactors from Plasma Heat, AnaTechMaz, pp.253