Gradient-based Model-free Optimization for Training Optical Computing Systems to Overcome Simulation-to-Reality Gaps

The core message of this paper is that a gradient-based model-free optimization (G-MFO) method can efficiently train optical computing systems in situ without relying on computationally heavy and biased system simulations, overcoming the simulation-to-reality gaps faced by conventional training approaches.