collaborators

12 papers

physics.plasm-ph2026

yancc: A GPU-accelerated, differentiable solver for neoclassical transport in tokamaks and stellarators

Rory Conlin, Matt Landreman

We present yancc, a new GPU-accelerated solver for the drift kinetic equation that computes neoclassical transport fluxes, flows, and currents in tokamaks and stellarators. The dri…

physics.plasm-ph2026

Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction

Matt Landreman, Michael Czekanski, Andrew Giuliani +2

Modern stellarators are typically designed by optimizing the shape of the plasma boundary surface, with the parameters taken to be Fourier amplitudes. Many promising optimization a…

physics.plasm-ph2026

Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications

Kaya Unalmis, Rahul Gaur, Rory Conlin +2

We present a fast, spectrally (exponentially) accurate, automatically differentiable bounce-averaging algorithm that is used to simplify kinetic models. Using this algorithm, imple…

physics.plasm-ph2026

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…

cs.LG2026

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…

physics.plasm-ph2026

Deflation Techniques for Stellarator Equilibrium and Optimization

Dario Panici, Byoungchan Jang, Rory Conlin +3

Stellarator optimization is a multi-objective, non-convex problem characterized by a complex objective landscape containing many local minima. The solution resulting from a single…