The implications of ZCS on the future development of physics-informed machine learning are profound. By addressing the challenges associated with computing derivatives in pointwise operators, ZCS opens up new possibilities for efficiently training neural networks to solve partial differential equations (PDEs) without compromising accuracy or model performance. The significant improvements in memory efficiency and training speed offered by ZCS can pave the way for tackling larger-scale and more complex physics-based problems using deep learning models.