Appendix A Implementation Details

We train all the networks for 500 epochs with Adam optimizer. The batch size is set to 32. For the backward pass, we use phantom gradients [ Geng et al., 2021 ] which are For the S-FNO-DEQ used in Table 1, we use Broyden's method [ Broyden, 1965 ] to solve for the The width of an FNO layer set to 32 across all the networks. Additionally, we retain only 12 Fourier modes in FNO layer, and truncate higher Fourier modes. As mentioned in Sec. 5 we use the dataset provided by Li et al. [ 2020a ] for our experiments with All the models are trained on 1024 data samples and tested on 500 samples.