ZCS stands out from other optimization techniques in machine learning by its ability to significantly reduce GPU memory consumption and training time for physics-informed operator learning. Unlike traditional methods like finite difference or analytical differentiation, ZCS simplifies the computation of high-order derivatives by introducing a zero-valued dummy scalar, leading to more efficient reverse-mode automatic differentiation. This reduction in memory usage and training time is crucial for complex problems involving high-dimensional data and functions.