CoilStellaration: A Dataset and Benchmark for Engineering-Aware Stellarator Coilset Generation

Stellarators are fusion energy devices that confine hot plasma using a carefully shaped magnetic field, and they are one of the most promising candidates in the race towards commercial fusion. Among the many systems that make up a stellarator, the electromagnetic coils generate the confining magnetic field. Their design is the key factor in deciding whether a stellarator can be practically engineered and cost‑effectively realized. Coilset design is an intrinsically complex problem: it does not admit either a closed or a unique solution, making it suitable for optimization. Coilset optimization is therefore at the heart of stellarator design: a coilset must accurately reproduce a target magnetic field while satisfying stringent engineering constraints on coil spacing, curvature, and length. Although recent datasets have accelerated progress on stellarator equilibria optimization, data-driven coilset design is limited by the lack of standardized datasets that pair target plasma configurations with engineering-aware coilsets. Here, we release a dataset (https://huggingface.co/datasets/proxima-fusion/coilstellaration) of 177,000 optimized stellarator coilsets targeting plasma configurations sampled from the publicly available ConStellaration dataset. For each configuration, we optimized coilsets to reproduce the magnetic field while satisfying a diverse set of engineering constraints. Alongside the dataset, we introduce a benchmark for conditional coilset generation: given a target plasma boundary and a set of engineering requirements, a model should produce a coilset that satisfies the engineering constraints while reproducing the target field. We provide reference code, evaluation scripts and baselines models trained on a subset of the provided data (https://github.com/proximafusion/coilstellaration). Beyond enabling better optimization initializations and one-shot coil design, our dataset provides a testbed for data-driven constrained optimization and for studying which plasma geometry features predict simpler, more engineerable coils. By releasing data, and code for benchmarks and baselines, we aim to lower the barrier for machine learning and optimization researchers to engage with stellarator coilset design and accelerate progress toward engineerable and cost-effective stellarators power plants.