Waveﬁeld solutions are critical for applications ranging from imaging to full waveform inversion. These wave-ﬁelds are often large, especially for 3D media, and multiple p o int sources, like Green’s functions. A recently introduced framework based on neu ra l networks admit-ting functional solutions to partial differential equations(PDEs) o↵ers the opportunity to solve the Helmholtz equation with a neural network (NN) model. The input to such an NN is a location in space and the output are the real and imaginary parts of the scattered waveﬁeldat that location, thus, acting like a function. The net-work is trained on random input points in space and a variance of the Helmh o lt z equation for the scatteredwaveﬁeld is used as the loss function to update the network parameters. In spite of the methods ﬂexibility, like handling irregular surfaces and complex media, and its potential for velocity model building, the cost of training the network far exceeds that of numerical solutions. Relying on the network’s ability to learn waveﬁeld features, we extend the dimension of this NN function to learn the waveﬁeld for many sources and frequencies, simultaneously. We show, in this preliminary study, that reasonable waveﬁeld solutions can be predicted using smaller networks. This includes waveﬁelds for frequencies not within the training range. The new NN function has the potential to eﬃciently represent the waveﬁeld as a function of location in space, as well as source location and frequency.