Spectrally accurate, reverse-mode differentiable bounce-averaging algorithm and its applications
Kaya E. Unalmis, Rahul Gaur, Rory Conlin, Dario Panici, Egemen Kolemen
TL;DR
The paper presents a spectrally accurate, reverse-mode differentiable bounce-averaging algorithm implemented in DESC to optimize stellarator performance. By deriving and differentiating the banana-regime neoclassical transport proxy $\epsilon_{\mathrm{eff}}^{3/2}$, the authors enable gradient-based optimization of finite-$\beta$ equilibria while preserving ideal MHD force balance. The approach combines inverse MHD solving, moving-grid spectral mappings, and advanced quadrature to achieve high accuracy and scalability, demonstrated on an OH equilibrium. This work provides a scalable framework for multi-objective stellarator optimization with potential extensions to other transport proxies and stability metrics.
Abstract
We present a fast, spectrally accurate, automatically differentiable bounce-averaging algorithm implemented in the DESC stellarator optimization suite. Using this algorithm, we can perform efficient optimization of many objectives to improve stellarator performance, such as the $ε_{\mathrm{eff}}^{3/2}$ proxy for the neoclassical transport coefficient in the $1/ν$ (banana) regime. By employing this differentiable approximation, for the first time, we optimize a finite-beta stellarator to directly reduce neoclassical ripple transport using reverse-mode differentiation. This ensures the cost of differentiation is independent of the number of controllable parameters.
