In recent years, data-driven operator approximation techniques have been explored as a means of solving physical problems described by ordinary and partial differential equations. In this paper, solutions to the linear 2D acoustic wave equation predicted by Fourier neural operator (FNO) networks are investigated in a square, free-field domain. The network’s ability to generalise over variable excitation source positions in unseen locations is investigated. Furthermore, the network is tasked with learning progressively longer solutions in time to assess how the ratio of input to output data affects network prediction accuracy. Error between ground truth and predicted simulations is quantified and examined in an acoustics context.