Generative Neural Network Based Non-Convex Optimization Using Policy Gradients with an Application to Electromagnetic DesignDownload PDF

24 Sept 2021, 16:11 (edited 01 Dec 2021)NeurIPS-AI4Science PosterReaders: Everyone
  • Keywords: reinforcement learning, machine learning, generative neural network, policy gradient, optimization, inverse design, adjoint method, physics, electromagnetics, photonics, grating coupler
  • TL;DR: A generative neural network based non-convex optimization algorithm using a one-step implementation of the policy gradient method is introduced and applied to grating coupler design, resulting in state-of-the-art performance.
  • Abstract: 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.
  • Track: Original Research Track
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