Generative Models For Science Lose Fidelity When Enforcing Hard Constraints

Generative models for science must respect hard constraints: PDEs, conservation laws, geometric feasibility. The standard recipe is to project at sampling time. It restores feasibility, but the projection drags samples into low-density regions of P_data, and fidelity drops.🧵