Despite advances in computational power, the cost of time-accurate flows in axial compressor and fan stages with spatially non-uniform inflow is still too high for design-stage use in industry. Body force modeling reduces the computation time to practical levels, mainly by reducing the problem to a steady one. These computations are important to determine efficiency penalties associated with non-uniform inflows. Previous studies of body force methods have, in most cases, relied on computations with the presence of the blades to calibrate loss models. In some recent studies, uncalibrated models have been used, but such models can drop off in accuracy at conditions where separation would occur on the blade surfaces. In this paper, a neural-network-based loss model introduced in a recent paper by the authors is implemented for NASA rotor 67 for both uniform and non-uniform inflow conditions. For uniform inflow, the spanwise trend of entropy variation is generally captured with the new body force model. Although there are discrepancies at some span fractions, the present model generally predicts the compressor’s isentropic efficiency to within 3 compared to bladed Reynolds-averaged Navier–Stokes simulations. For non-uniform inflow, we consider a stagnation pressure profile representative of boundary layer ingestion. The results show that the region of maximum entropy generation is captured by the present model and the prediction of isentropic efficiency penalty due to the non-uniform inflow is only 0.2 points less than that determined from bladed computations.