ConStellaration: A dataset of QI-like stellarator plasma boundaries and optimization benchmarks

2025-06-24

AI Summary

To address the lack of standardized optimization frameworks and high-quality benchmark datasets for quasi-isodynamic (QI) stellarator design, this work introduces the first publicly available, diverse dataset of QI stellarator boundary shapes, accompanied by ideal MHD equilibrium solutions and key performance metrics. We define three progressive optimization benchmark tasks—geometric constraint satisfaction, engineering feasibility, and multi-objective trade-off—and propose a unified generative framework integrating classical optimization, ideal MHD simulation, and data-driven modeling, enabling rapid generation of physically valid configurations without expensive physics simulations. This provides the first end-to-end open benchmark and strong baseline models for ML and optimization researchers, substantially lowering entry barriers. Experiments demonstrate that learned models efficiently generate novel, physically feasible stellarator configurations, accelerating interdisciplinary fusion energy research.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability
• Machine Learning: Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Stellarators are magnetic confinement devices under active development 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 made stellarator optimization more accessible. However, broader 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, considered as a promising path to commercial fusion due to their inherent resilience to 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 a variety of QI fields and optimizing corresponding stellarator plasma boundaries. We introduce three optimization benchmarks of increasing complexity: (1) a single-objective geometric optimization problem, (2) a "simple-to-build" QI stellarator, and (3) a multi-objective ideal-MHD stable QI stellarator that investigates trade-offs between compactness and coil simplicity. For every benchmark, we provide reference code, evaluation scripts, and strong baselines based on classical optimization techniques. Finally, we show how learned models trained on our dataset can efficiently generate novel, feasible configurations without querying expensive physics oracles. By openly releasing the dataset along with benchmark problems and baselines, we aim to lower the entry barrier for optimization and machine learning researchers to engage in stellarator design and to accelerate cross-disciplinary progress toward bringing fusion energy to the grid.

Problem

• Lack of standardized stellarator optimization benchmarks and datasets
• Need for data-driven approaches in quasi-isodynamic stellarator design
• High computational cost and expertise barriers in stellarator optimization

Innovation

• Open dataset of QI-like stellarator boundaries
• Three optimization benchmarks with baselines
• Learned models generate feasible configurations