97 citations · 158 across the 6 of their papers we have counts for
6 papers
Deep electric field predictions by drift-reduced Braginskii theory with plasma-neutral interactions based upon experimental images of boundary turbulence
Abhilash Mathews, Jerry Hughes, James Terry +1
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 axis…
Turbulent field fluctuations in gyrokinetic and fluid plasmas
Abhilash Mathews, Noah Mandell, Manaure Francisquez +2
A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertaint…
Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression
Abhilash Mathews, Jerry Hughes
The edge density and temperature of tokamak plasmas are strongly correlated with energy and particle confinement and their quantification is fundamental to understanding edge dynam…
Constructing a new predictive scaling formula for ITER's divertor heat-load width informed by a simulation-anchored machine learning
C. S. Chang, S. Ku, R. Hager +6
Understanding and predicting divertor heat-load width is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predic…
Gyrokinetics investigations of an I-mode pedestal on Alcator C-Mod
Xing Liu, M. Kotschenreuther, D. R. Hatch +3
Naturally stable to ELMs, and with widths larger than EPED predictions, the I-modes are an excellent laboratory for investigating the role of drift micro-instabilities in pedestals…
A fast low-to-high mode bifurcation dynamics in a tokamak edge plasma gyrokinetic simulation
C. S. Chang, S. Ku, G. R. Tynan +6
Transport barrier formation and its relation to sheared flows in fluids and plasmas are of fundamental interest in various natural and laboratory observations and of critical impor…