数据驱动的紧凑型准等力 stellarators（准等磁面仿星器）生成
Data-Driven Generation of Compact Quasi-Isodynamic Stellarators
AI总结：
该研究基于ConStellaration数据库，扩展条件边界生成至四场周期QI构型，通过数据调整将紧凑型域测试损失降约87%，生成符合条件的超紧凑型仿星器候选，为QI优化提供种子。
AI中文摘要：
仿星器设计需探索庞大的三维等离子体边界空间，其中仅小部分能产生可用的平衡态。数据驱动模型可通过学习现有优化构型缩小该搜索范围。基于ConStellaration数据库，我们将条件边界生成扩展到四场周期的QI（准等力）构型，聚焦于采样稀疏的低纵横比区域。该方法学习已知QI平衡态的共同几何结构，再利用小型高保真紧凑型数据集对其进行调整，使目标磁性能能指导生成超出原始数据分布的结果。这种调整将紧凑型域的测试损失降低约87%，并产生符合规定条件的收敛超紧凑型候选构型。部分候选构型展现出良好的约束指标，其中一个为后续QI优化和有限β评估提供了有用的种子。因此，该方法可作为高保真物理与优化的数据驱动前端。
英文摘要：
Stellarator design explores a vast space of three-dimensional plasma boundaries, only a small fraction of which yields usable equilibria. Data-driven models can narrow this search by learning from existing optimized configurations. Building on the ConStellaration database, we extend conditional boundary generation to four-field-period QI configurations, focusing on the sparsely sampled low-aspect-ratio regime. The approach learns the common geometric structure of known QI equilibria and then adapts it using a small high-fidelity compact dataset, allowing target magnetic properties to guide generation beyond the original data distribution. This adaptation reduces the compact-domain test loss by approximately 87% and yields converged ultra-compact candidates consistent with the prescribed conditions. Several candidates show favorable confinement indicators, and one provides a useful seed for further QI optimization and finite-beta assessment. The method therefore serves as a data-informed front end to high-fidelity physics and optimization.
