Neural operators don't need to be accurate to be useful—they can guide PINNs away from spurious solutions by providing the right structural prior, enabling reliable PDE solving in regimes where either method alone would fail.
This paper shows how to combine neural operators (fast but inaccurate) with physics-informed neural networks (accurate but optimization-fragile) to solve PDEs reliably. An imperfect neural operator provides a structural hint about which solution the PINN should find, while the PDE residual refines it to high accuracy.