To include ImW , an additional residual term for the imaginary component could be added to our loss function. Moreover, more advanced architectures might be required for effective learning, such as using separate trunk networks for the real and imaginary parts. The model is trained using an Adam optimizer with a learning rate of 0.001 and batch size of 20 for 120, 000 epochs. A plot of model performance can be found in Figure D.13. Rather than training with all points in our spatial grid (N k × N t = 512 × 501 ∼ 256, 000) for each batch, we randomly select 10, 000 points for training. We reserve 20% of our 500 experiments for testing. Figure shows a visualization of the DeepONet performance. Error tends to concentrate around the initial conditions, which can be attributed to the fact that the initial conditions are randomly drawn from a uniform distribution and are therefore challenging for a NN (a continuous interpolator) to learn. Additionally, the high-frequency solutions (low κ values) are not learned well. This issue could potentially be addressed by using a multi-stage neural net for the branch network and will be taken up as future work.