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Exascale supercomputing boosts artificial intelligence to simulate extreme plasma and reduce uncertainties in nuclear fusion and astrophysical phenomena research, with significant gains in speed and precision in analyzing complex turbulence in highly chaotic systems.
The Frontier supercomputer, installed at Oak Ridge National Laboratory in the United States, is being used to train artificial intelligence models capable of predicting plasma turbulence with much greater speed and precision, in research that could support studies on supernovae, space weather, and future nuclear fusion reactors.
The machine, linked to the U.S. Department of Energy, ranks second on the latest TOP500 list, behind only El Capitan, also American.
Both are part of the exascale generation, consisting of systems capable of performing at least one quintillion calculations per second in performance tests.
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In Frontier’s case, its computational capacity allowed it to generate thousands of detailed plasma simulations and train a combination of AI models aimed at reproducing magnetohydrodynamic turbulence, a phenomenon associated with the behavior of electrically charged fluids under the influence of magnetic fields.
AI accelerates plasma simulations in seconds
Research released by ORNL shows that the system can produce detailed turbulence predictions in a few seconds and reduce errors by more than half compared to previous approaches.
The gain is significant because turbulence often concentrates precisely the details most difficult to represent in traditional simulations.
Plasma is a state of matter formed by electrically charged particles, common in stars and nuclear fusion experiments.
Because it reacts to magnetic fields, it can be confined in experimental equipment, but small instabilities still hinder the control of processes that require extreme temperatures.
To address this problem, researchers combined physics-informed neural operators with generative diffusion models.
The first stage captures the general evolution of the plasma, while the second reconstructs smaller structures, such as eddies and rapid variations that define turbulence.
Without this division, AI models tend to smooth out important details and lose some of the information needed to understand chaotic systems.
Frontier was specifically brought in to enable large-scale training, something that required a large volume of data and high processing power.
From star behavior to nuclear fusion
The study has a direct impact on astrophysics, because magnetic turbulence appears in phenomena such as supernovae, star formation, galaxy dynamics, and the interaction between plasmas and magnetic fields.
The better the simulation, the greater the ability to test extreme scenarios without relying solely on indirect observations.
Eliu Huerta, a computational scientist at Argonne National Laboratory, stated that this type of capability was a long-standing ambition for astrophysicists and other researchers.
According to him, it is the first time that AI has reached this level of understanding in systems of such complexity.
The sentence summarizes the difficulty of the problem.
The more chaotic the system, the more expensive and time-consuming it becomes to reproduce its evolution on computers, especially when the goal is to preserve small structures that influence the overall behavior of the plasma.
Beyond the interest in cosmic events, technology can help in the development of nuclear fusion reactors, which are still in the experimental phase.
In these devices, the goal is to fuse light nuclei, such as deuterium and tritium, to release energy in a controlled manner.
Plasma turbulence is a central challenge for nuclear fusion
Nuclear fusion requires extremely hot plasma, with temperatures that can reach around 150 million degrees Celsius in projects based on the reaction between deuterium and tritium.
Under this condition, no solid material can directly touch the fuel, which makes magnetic confinement a central piece of the technology.
The challenge is not just reaching the necessary temperature.
Researchers can already heat plasmas to levels compatible with fusion experiments, but maintaining stability for long enough, with low energy loss, remains one of the major obstacles to transforming the technique into a commercial source.
When turbulence increases, the plasma loses confinement, alters its density in critical regions, and reduces the reaction’s efficiency.
Therefore, quickly predicting instabilities can improve experiment design, guide operational adjustments, and reduce reliance on slower methods.
The application developed with Frontier does not yet represent a functioning commercial fusion reactor.
The advance lies in modeling: by delivering faster predictions with less error, AI offers a tool to investigate scenarios that would be difficult to explore with conventional simulations alone.
Frontier remains among the world’s most powerful supercomputers
Frontier was the first supercomputer officially recognized at exascale on the TOP500 list in 2022, and remains among the most powerful systems in the world.
In the most recent available edition of the ranking, El Capitan leads, followed by Frontier and Aurora, all installed in laboratories linked to the U.S. Department of Energy.
This concentration shows the strategic importance of high-performance computing for areas such as energy, physics, climate, materials, and national security.
Machines of this caliber are not just for executing faster calculations, but for enabling scientific questions that were previously impractical.
In the ORNL project, supercomputing was used to produce high-fidelity databases and train models that respect the physical equations involved.
This combination seeks to prevent AI from merely recognizing superficial patterns, bringing the result closer to the real conditions studied.
Researchers expect to expand the model for more complex simulations, including complete three-dimensional representations of plasma and more demanding astrophysical scenarios.
Among the cited applications is the modeling of turbulence in plasmas used in nuclear fusion, an area where precision and speed can shorten research stages.
For now, the result reinforces a trend in computational science: supercomputers and AI are increasingly working together to investigate extreme phenomena, from stellar explosions to experimental fusion chambers.
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