5 papers
AI-Accelerated Gyrokinetic Predictions of Turbulent Transport for Stellarator Design Optimization and Experimental Planning
R. Michael Churchill, Matt Landreman, Jong Youl Choi +10
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellara…
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…
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…
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…
Grad-Shafranov equilibria via data-free physics informed neural networks
Byoungchan Jang, Alan A. Kaptanoglu, Rahul Gaur +3
A large number of magnetohydrodynamic (MHD) equilibrium calculations are often required for uncertainty quantification, optimization, and real-time diagnostic information, making M…