Deep Learning Enhances Understanding of Turbulence in Plasmas Through Simulation.
In a way, artificial intelligence has developed capabilities that surpass those of humans, particularly in data analysis, enabling a deeper understanding of complex phenomena. In this study, it allowed researchers to observe the functioning of future fusion reactors.
The Complexity of Nuclear Fusion
By combining deep learning and physics, researchers from MIT have gained a better understanding of turbulence in plasmas, which is a crucial element for achieving nuclear fusion. Plasma is the fourth state of matter, following solid, liquid, and gas, and constitutes more than 99.9% of the universe.
One approach involves confining it within devices known as tokamaks to heat it sufficiently to release kinetic energy, which will then fuel new fusion reactions. Tokamaks utilize magnetic fields to control the particles that make up the plasma. However, a challenge arises due to the instability of the plasma, which may damage the reactor components.
Simulation and Deep Learning to Understand Turbulence in Plasmas
To analyze turbulence, physicists have thus far employed the reduced two-fluid theory of Braginskii. This involves simulating boundary plasmas in experiments to predict temperature, density, electric potential, and fluxes.
In a new study, MIT researchers combined physics with deep learning to test the accuracy of this reduced model, focusing particularly on the electric field and electronic pressure. In other words, the relationship between these variables defines the theory of turbulence in plasmas, and deep learning has helped them verify it.
They demonstrated compatibility between the turbulent electric fields predicted by the model and high-fidelity gyrokinetic predictions in plasmas relevant to existing devices.
They also discovered that the new deep learning technique allows for diagnosing previously unknown turbulent field fluctuations. In other words, even with a limited number of observations, it is possible to obtain a wealth of information.
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