TNOs separate the fixed topological rules governing information flow from learned transformations, enabling neural operators that respect physical geometry and conservation laws—useful for solving PDEs on irregular domains where standard approaches struggle.
This paper introduces Topological Neural Operators (TNOs), a framework for learning operators on complex geometric structures by explicitly modeling how information flows across different dimensional features (points, edges, faces, etc.) using topological calculus.