114 citations · 499 across the 15 of their papers we have counts for
16 papers
Analytic non-axisymmetric 3D MHD equilibria without stellarator symmetry
Opal Issan, Jonathan Citrin, Matt Landreman +1
Analytical solutions to the three-dimensional ideal magnetohydrodynamic equilibrium equations and pro…
Duqtools: Dynamic uncertainty quantification for Tokamak reactor simulations modelling
Victor Azizi, Stef Smeets, Florian Koechl +3
Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computatio…
TORAX: A Fast and Differentiable Tokamak Transport Simulator in JAX
Jonathan Citrin, Ian Goodfellow, Akhil Raju +11
We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat…
Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles
L. Zanisi, A. Ho, T. Madula +7
Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential…
Fast transport simulations with higher-fidelity surrogate models for ITER
J. Citrin, P. Trochim, T. Goerler +3
A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The model consists of a set of neural networks trained on a bespoke quasilinear GENE…
How accurate are flux-tube (local) gyrokinetic codes in modeling energetic particle effects on core turbulence?
A. Di Siena, T. Hayward-Schneider, P. Mantica +8
Flux-tube gyrokinetic codes are widely used to simulate drift-wave turbulence in magnetic confinement devices. While a large number of studies show that flux-tube codes provide an…
Integrated modelling and multiscale gyrokinetic validation study of ETG turbulence in a JET hybrid H-mode scenario
J Citrin, S Maeyama, C Angioni +11
Previous studies with first-principle-based integrated modelling suggested that ETG turbulence may lead to an anti-GyroBohm isotope scaling in JET high-performance hybrid H-mode sc…
The Problem of Marginality in Model Reductions of Turbulence
C. Gillot, G. Dif-Pradalier, Y. Sarazin +8
Reduced quasilinear (QL) and nonlinear (gradient-driven) models with scale separations, commonly used to interpret experiments and to forecast turbulent transport levels in magneti…
2022 Review of Data-Driven Plasma Science
Rushil Anirudh, Rick Archibald, M. Salman Asif +60
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data…
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…
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…
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…
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…
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…
Fast modeling of turbulent transport in fusion plasmas using neural networks
Karel Lucas van de Plassche, Jonathan Citrin, Clarisse Bourdelle +7
We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 mill…
Global scaling of the heat transport in fusion plasmas
Sara Moradi, Johan Anderson, Michele Romanelli +2
A global heat flux model based on a fractional derivative of plasma pressure is proposed for the heat transport in fusion plasmas. The degree of the fractional derivative of the he…