用智能演化法自动发现高效可重构电源电路，性能远超传统设计。
PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters
- 基于解析建模与演化优化，无需仿真即可评估电路性能。
- 发现新型8模式电路，性能比最优训练样本高23%。
- 适合电力电子、芯片设计等领域的自动化电路创新者。
发现高性能电路拓扑需面对指数级增长的设计空间，传统方法依赖专家经验。现有AI方法或局限于预设模板，或生成新拓扑规模有限且缺乏严格验证，导致大规模性能驱动型发现仍不充分。本文提出PowerGenie框架，实现可重构电源转换器的规模化高性能自动发现。该框架引入：(1) 自动化解析方法，无需元件参数设定或SPICE仿真即可判断转换器功能与理论性能极限；(2) 演化微调机制，通过适应度选择与唯一性验证，协同优化生成模型与其训练分布。相比现有方法易出现模式坍缩和过拟合，本方案在语法正确性、功能有效性、新颖率及性能指标（FoM）上均更优。PowerGenie发现一种新型8模式可重构转换器，其FoM较最佳训练拓扑提升23%。SPICE仿真验证其在8个模式下平均绝对效率提升10%，单模式最高达17%。代码将在发表后公开。
原文摘要 · Abstract (English)
Discovering superior circuit topologies requires navigating an exponentially large design space-a challenge traditionally reserved for human experts. Existing AI methods either select from predefined templates or generate novel topologies at a limited scale without rigorous verification, leaving large-scale performance-driven discovery underexplored. We present PowerGenie, a framework for automated discovery of higher-performance reconfigurable power converters at scale. PowerGenie introduces: (1) an automated analytical framework that determines converter functionality and theoretical performance limits without component sizing or SPICE simulation, and (2) an evolutionary finetuning method that co-evolves a generative model with its training distribution through fitness selection and uniqueness verification. Unlike existing methods that suffer from mode collapse and overfitting, our approach achieves higher syntax validity, function validity, novelty rate, and figure-of-merit (FoM). PowerGenie discovers a novel 8-mode reconfigurable converter with 23% higher FoM than the best training topology. SPICE simulations confirm average absolute efficiency gains of 10% across 8 modes and up to 17% at a single mode. Code will be released upon publication.
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