Generative Neural Network Based Non-Convex Optimization Using Policy Gradients with an Application to Electromagnetic Design
A generative neural network based non-convex optimization algorithm using a one-step implementation of the policy gradient method is introduced and applied to electromagnetic design. We demonstrate state-of-the-art performance of electromagnetic devices called grating couplers, with key advantages over local gradient-based optimization via the adjoint method.
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