Differentiable optimization for automated optical design

Differentiable optical design builds optical simulations using automatic differentiation frameworks, enabling the automated and accurate computation of gradients for all optical parameters via a single backpropagation step. This approach fully leverages AI-driven optimization algorithms and GPU parallel acceleration. Compared to classical optical design methods, differentiable optical design offers numerous advantages, including automated optimization and stable convergence, demonstrating its potential to become the foundation for the next generation of optical design software.