SCOPE: Expanding Generative Coverage for STELLARATOR Design

Stellarator design for fusion requires multiple plasma configurations that satisfy requested properties and provide alternatives for subsequent physics and engineering development. Finding these configurations is expensive, and high-quality training designs are scarce. Generative models can accelerate candidate production, creating an opportunity to expand their repertoire by learning from new physical evaluations. This introduces a mismatch between discovery and learning: once a design enters the search archive, it ceases to be novel, while the generator may still need further training to learn its region. We introduce SCOPE, a framework that separates novelty for current exploration from the continued learning value of discoveries. SCOPE starts from a conditional flow model of full plasma boundaries trained on physically screened designs. In each round, generated candidates are checked for equilibrium, request satisfaction, and physical quality, and compared against a growing archive to assess novelty. This feedback guides policy updates toward novel, useful designs, while valid discoveries retain training priority after archival. Experiments show that SCOPE generates higher-quality candidates and broadens exploration within the target design domain. It produces 2.4 times as many distinct qualified designs beyond current baseline in ordinary generation. In online search, SCOPE is able to discover more valid and qualified designs in controlled budget.