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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.
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design).
Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity
By simulating Ising machines with polynomial, periodic, sigmoid and clipped transfer functions and benchmarking them with MaxCut optimization problems, we find the choice of transfer function to have a significant influence on the calculation time and solution quality.
Applied Physics Emerging Technologies Computational Physics
Towards Trainable Media: Using Waves for Neural Network-Style Training
In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier.
