33 citations · 93 across the 22 of their papers we have counts for
7 papers · 1 filter
Narrow Operator Models of Stellarator Equilibria in Fourier Zernike Basis
Timo Thun, Rory Conlin, Dario Panici +1
Numerical computation of the ideal Magnetohydrodynamic (MHD) equilibrium magnetic field is at the base of stellarator optimisation and provides the starting point for solving more…
Exponential Spectral Scaling: Robust and Efficient Stellarator Boundary Optimization via Mode-Dependent Scaling
Byoungchan Jang, Rory Conlin, Matt Landreman
Stellarator boundary optimization faces a fundamental numerical challenge: the extreme disparity between low- and high-mode amplitudes creates an optimization landscape in which di…
Surface Current Optimization and Coil-Cutting Algorithms for Stage-Two Stellarator Optimization
Dario Panici, Rory Conlin, Rahul Gaur +8
Stellarator optimization often takes a two-stage approach, where in the first stage the boundary is varied in order to optimize for some physics metrics, while in the second stage…
Improving ideal MHD equilibrium accuracy with physics-informed neural networks
Timo Thun, Andrea Merlo, Rory Conlin +2
We present a novel approach to compute three-dimensional Magnetohydrodynamic equilibria by parametrizing Fourier modes with artificial neural networks and compare it to equilibria…
Numerical asymptotics of near-axis expansions of quasisymmetric magnetohydrostatic equilibria with anisotropic pressure
Lanke Fu, Eduardo Rodriguez, Rory Conlin +1
Quasisymmetry (QS) is a property of special magnetic configurations, where the magnetic field strength, but not necessarily the full vector field, has a direction of symmetry. QS l…
Extending near-axis equilibria in DESC
Dario Panici, Eduardo Rodriguez, Rory Conlin +2
The near-axis description of optimised stellarator fields has proven to be a powerful tool both for design and understanding of this magnetic confinement concept. The description c…
How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning
Matt Landreman, Jong Youl Choi, Caio Alves +4
Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods…