activity
20192026
most citedThe data-driven future of high energy density physics

114 citations · 499 across the 15 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

physics.comp-ph2021★ 18 cited

Two step clustering for data reduction combining DBSCAN and k-means clustering

Bart J. J. Kremers, Aaron Ho, Jonathan Citrin +1

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of…

physics.plasm-ph2021★ 114 cited

The data-driven future of high energy density physics

Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson +20

The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fu…

physics.plasm-ph2021★ 64 cited

Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling

Aaron Ho, Jonathan Citrin, Fulvio Auriemma +7

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques w…

physics.plasm-ph2021★ 39 cited

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Aaron Ho, Jonathan Citrin, Clarisse Bourdelle +5

Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neur…

physics.plasm-ph2021

Quasilinear gyrokinetic theory: A derivation of QuaLiKiz

Cole Darin Stephens, Xavier Garbet, Jonathan Citrin +3

In order to predict and analyze turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been…