Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
The method introduces a novel kernel-based operator learning technique that learns surrogate models for incompressible fluid flows by embedding physical constraints directly into the mathematical basis functions. This approach analytically enforces incompressibility, periodicity, and turbulence modeling simultaneously. The resulting framework offers significantly higher accuracy and trains much faster than traditional neural operators.