Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning

ArXi:2604.13133v1 Announce Type: new Thermodynamic cycles are pivotal in determining the efficacy of energy conversion systems. Traditional design methodologies, which rely on expert knowledge or exhaustive enumeration, are inefficient and lack scalability, thereby cons