Funding information Kong Christian den Tiendes Fond; Harboefonden; Eva og Henry Fraenkels Mindefond; VILLUM FONDEN Purpose: Rapid 2DRF pulse design with subject-specific B + 1 inhomogeneity and B 0 off-resonance compensation at 7 T predicted from convolutional neural networks is presented. Methods: The convolution neural network was trained on half a million singlechannel transmit 2DRF pulses optimized with an optimal control method using artificial 2D targets, B + 1 and B 0 maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured B + 1 and B 0 maps from a high-resolution gradient echo sequence. Results: Pulse prediction by the trained convolutional neural network was done on the fly during the MR session in approximately 9 ms for multiple hand-drawn regions of interest and the measured B + 1 and B 0 maps. Compensation of B + 1 inhomogeneity and B 0 off-resonances has been confirmed in the phantom and in vivo experiments. The reconstructed image data agree well with the simulations using the acquired B + 1 and B 0 maps, and the 2DRF pulse predicted by the convolutional neural networks is as good as the conventional RF pulse obtained by optimal control. Conclusion: The proposed convolutional neural network-based 2DRF pulse design method predicts 2DRF pulses with an excellent excitation pattern and compensated B + 1 and B 0 variations at 7 T. The rapid 2DRF pulse prediction (9 ms) enables subjectspecific high-quality 2DRF pulses without the need to run lengthy optimizations. K E Y W O R D S 2DRF pulses, 7 T, artificial intelligence, deep learning, optimal control 1 | INTRODUCTION Multidimensional spectral-/spatial-selective RF pulses have applications in inner-volume imaging, 1,2 navigation, 3,4 fMRI, 5,6 DWI, 7 DTI, 8 MRS, 9 carbon-13 dissolution dynamic nuclear polarization, 10,11 flow imaging, 12,13 and suppression. 14 RF pulse designers in general navigate between matters of pulse types; spatial (and/or spectral) target profiles; inhomogeneous B + 1 and B 0 fields; underlying gradient waveforms; system imperfections 15 ; constraints (eg, power, 16 specific absorption rate [SAR], 17 and states 18,19); and finally, which pulse design tool and physics model to adopt for the task