ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks
Santiago Cadena, Andrea Merlo, Emanuel Laude, Alexander Bauer, Atul Agrawal, Maria Pascu, Marija Savtchouk, Lukas Bonauer, Enrico Guiraud, Stuart Hudson, Markus Kaiser
Stellarators are magnetic confinement devices that are being pursued to deliver steady-state carbon-free fusion energy. Their design involves a high-dimensional, constrained optimization problem that requires expensive physics simulations and significant domain expertise. Recent advances in plasma physics and open- source tools have facilitated the process of stellarator optimization. However, wide-spread community progress is currently bottlenecked by the lack of standardized optimization problems with strong baselines and datasets that enable data-driven approaches, particularly for quasi-isodynamic (QI) stellarator configurations, seen as a promising path to commercial fusion due to their intrinsic avoidance of current-driven disruptions. Here, we release an open dataset of diverse QI-like stellarator plasma boundary shapes, paired with their ideal magnetohydrodynamic (MHD) equilibria and performance metrics. We generated this dataset by sampling diverse QI fields and optimizing stellarator plasma boundaries to target key properties. We propose three optimization benchmarks of varying complexity involving a single-objective constrained geometric problem; a “simple-to-build” single-objective QI stellarator; and a multi-objective QI stellarator that is also MHD stable. For every benchmark, we provide reference code, evaluation scripts, and strong baselines based on classical optimization methods. By openly releasing the dataset (https://huggingface.co/datasets/proxima-fusion/ constellaration) together with benchmark problems and baselines (https://github.com/proximafusion/constellaration), we aim to lower the barrier for optimization and machine learning researchers to contribute to stellarator design, and to accelerate cross-disciplinary progress toward bringing fusion energy to the grid.