The super-powerful exaflop supercomputer Frontier performs quintillion operations per second to take control of the most unpredictable phenomena.
American researchers have faced one of the most complex challenges in computational physics: magnetohydrodynamic turbulence in plasma. To solve it, they utilized Frontier, one of the most powerful exaflop supercomputers in the world.
Plasma is an ionized gas, the particles of which carry an electric charge and respond sensitively to magnetic fields. Magnetohydrodynamic, or MHD turbulence, describes the chaotic motion of this medium under the influence of a magnetic field.
It is responsible for grand phenomena such as solar flares, supernova explosions, and the behavior of plasma in nuclear fusion reactors. However, accurately calculating these processes has always been extremely challenging.
This ambitious project was carried out by a team from the Oak Ridge National Laboratory under the U.S. Department of Energy. For calculations, the researchers used not only Frontier but also advanced artificial intelligence models.
The authors are confident that the new scheme will significantly improve plasma modeling and refine supernova calculations. In the long term, this will aid in the development of more efficient nuclear fusion reactors.
The main difficulty lies in the fact that turbulent plasma manifests itself on multiple scales simultaneously. Within the flow, large structures, small vortices, rapid local oscillations, and sharp parameter fluctuations arise at the same time.
All these elements constantly interact with each other, so even a slight loss of detail quickly distorts the entire picture. Because of this, physicists find it challenging to create a model that is both computationally feasible and retains the important properties of the real process.
Conventional methods often resort to simplifications; for example, the Reynolds-Averaged Navier-Stokes approach averages the flow behavior. This smooths out small structures, which is sufficient for a number of engineering tasks.
However, when working with plasma, such a compromise eliminates part of the important physics. The model conveys the overall development of the system but poorly describes vortices, rapid fluctuations, and small-scale flows that significantly affect plasma behavior.
The team divided the task into two key stages. First, a neural operator, taking into account the physical equations, was trained to describe the overall development of plasma over time.
This type of model does not simply look for statistical matches in the data but builds a forecast based on the laws governing the system. Then, a diffusion model restored the smaller structures that are usually lost during simplification.
These include small vortices, local flows, and rapid oscillations. Thus, the first model set the general course of the process, while the second returned the lost details.
Training this complex linkage required colossal computational resources. The Frontier supercomputer was used to generate high-precision datasets, on which both models were then trained.
In other words, Frontier first created detailed numerical simulations of plasma. Only then did these results serve as the basis for training the artificial intelligence.
This stage allowed researchers to achieve a level of detail that was previously considered almost unattainable. According to the team, the final system is capable of making very detailed turbulence forecasts in a matter of seconds.
It reduces the error by more than half compared to previous methods. The authors emphasize that the model not only accelerates calculations but also strives to accurately reproduce the physics of the process.
It maintains a connection with the equations while simultaneously restoring the complexity of plasma that older approaches typically smoothed out. The practical value of this work extends far beyond a single computational task.
More accurate models are necessary for astrophysicists to calculate supernovae and other extreme processes. In these, plasma and magnetic fields determine the course of events.
The same approach can also assist in nuclear fusion tasks, where turbulence directly affects plasma stability and energy losses in the reactor. The next stage includes more complex systems.
These are full three-dimensional plasma simulations, more complex astrophysical environments, and new calculations for nuclear fusion facilities. If this approach withstands scaling, Frontier and similar machines could significantly accelerate not only computations but also the search for new physical models.
