To adapt ZCS for use in structured grid-based models like Convolutional Neural Networks (CNNs), certain considerations need to be taken into account. While CNNs inherently satisfy translation invariance conditions required for AD, incorporating ZCS can further optimize memory usage and computational efficiency by feeding two scalars instead of full coordinates as leaf variables during backpropagation. By leveraging ZCS within CNN architectures on structured grids, researchers can enhance the performance of physics-informed machine learning models while maintaining compatibility with grid-based data structures.