[2601.21984] PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters

Computer Science > Machine Learning arXiv:2601.21984 (cs) [Submitted on 29 Jan 2026 (v1), last revised 25 Mar 2026 (this version, v3)] Title:PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters Authors:Jian Gao, Yiwei Zou, Abhishek Pradhan, Wenhao Huang, Yumin Su, Kaiyuan Yang, Xuan Zhang View a PDF of the paper titled PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters, by Jian Gao and 6 other authors View PDF HTML (experimental) Abstract: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, no...