Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence
This addresses plasma turbulence modeling for fusion energy research, offering a novel approach to integrate theory and data without explicit boundary conditions, though it is incremental in applying deep learning to a specific domain.
The paper tackled predicting turbulent electric fields in fusion plasmas by combining drift-reduced Braginskii theory with experimental data using physics-informed deep learning, finding that including plasma-neutral interactions strengthened correlations between electric field and electron pressure and increased turbulent amplitudes and shearing rates.
We present 2-dimensional turbulent electric field calculations via physics-informed deep learning consistent with (i) drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field and (ii) experimental estimates of the fluctuating electron density and temperature on open field lines obtained from analysis of gas puff imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects from the locally puffed atomic helium on particle and energy sources within the reduced plasma turbulence model are found to strengthen correlations between the electric field and electron pressure. The neutrals are also directly associated with broadening the distribution of turbulent field amplitudes and increasing ${\bf E \times B}$ shearing rates. This demonstrates a novel approach in plasma experiments by solving for nonlinear dynamics consistent with partial differential equations and data without encoding explicit boundary nor initial conditions.