Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

ArXi:2603.24500v1 Announce Type: new Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we