Numerical solvers for partial differential equations (PDEs) are accurate, but expensive when many related problem instances must be solved. Neural operators offer a faster alternative by learning mappings from input fields to solution fields. However, standard Fourier Neural Operator (FNO) training still depends on labelled simulation data, which must be generated by a solver. This study examines whether physics-informed training can improve data efficiency for neural-operator PDE surrogates. A data-driven FNO is compared with a Physics-Informed Neural Operator (PINO) using the same FNO backbone. This keeps the comparison centred on the training objective rather than the architecture. PINO adds a PDE-residual penalty to the supervised loss, encouraging predictions to satisfy the governing equation as well as match labelled data. The experiments use two PDEBench benchmarks: 2D Darcy flow and 1D Burgers' equation. Accuracy is measured with relative L₂ error, and physical consistency with PDE residual. PINO reached lower relative L₂ error than FNO on both equations at every tested fraction. It also surpassed the full-data FNO using 10% of the labels on Darcy flow and 50% on Burgers' equation. PINO reduced PDE residuals, especially for Darcy flow. However, the out-of-distribution tests showed that better in-distribution accuracy did not imply robust extrapolation. Overall, physics-informed training improved in-distribution data efficiency and physical consistency, but the gain depended on the equation, labelled-data budget, physics-loss weight and evaluation regime. ... Read more