87 citations · 225 across the 11 of their papers we have counts for
11 papers
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment
Abhilash Mathews
Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-b…
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…
Deep modelling of plasma and neutral fluctuations from gas puff turbulence imaging
A. Mathews, J. L. Terry, S. G. Baek +8
The role of turbulence in setting boundary plasma conditions is presently a key uncertainty in projecting to fusion energy reactors. To robustly diagnose edge turbulence, we develo…
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…
Evidence of a shared spectro-temporal law between sources of repeating fast radio bursts
Mohammed A. Chamma, Fereshteh Rajabi, Christopher M. Wyenberg +2
We study the spectro-temporal characteristics of two repeating fast radio bursts (FRBs), namely, FRB 20180916B and FRB 20180814A, and combine the results with those from our earlie…