PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints

ArXi:2603.08283v1 Announce Type: new Real-world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and thus learning models that incorporate physical and geometric knowledge, i.e., physics-informed machine learning (PIML), offer a promising pathway for efficient solution. Here, we