Efficient modelling of permanent magnet field distribution for deep learning applications

•We represented the axial, azimuthal and radial components of the magnetic field created by permanent magnets and uniform magnetisations in semi-analytical expressions (SAE).•We built a machine learning model using the training data generated by the SAE.•The machine learning model can predict the field distributions of permanent magnets in various configurations.•The predicted magnetic field using the machine learning model is in good agreement with the ground-truth.•The machine learning model is much faster than the SAE and Finite element method in terms of computational time.