Operator Learning with Domain Decomposition for Geometry Generalization in PDE Solving

Researchers propose a new framework called Operator Learning with Domain Decomposition to solve partial differential equations (PDEs) on arbitrary geometries using neural operators. The approach addresses data efficiency and geometry generalization challenges by breaking complex domains into smaller subdomains that can be solved locally and then combined into global solutions.