Search Results for author: Thomas Van Vaerenbergh

Found 4 papers, 0 papers with code

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

no code implementations NeurIPS Workshop AI4Scien 2021 Sean Hooten, Sri Krishna Vadlamani, Raymond G. Beausoleil, Thomas Van Vaerenbergh

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

no code implementations30 Jun 2021 Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh

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).

reinforcement-learning Reinforcement Learning (RL) +1

Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity

no code implementations18 Dec 2020 Fabian Böhm, Thomas Van Vaerenbergh, Guy Verschaffelt, Guy Van der Sande

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

no code implementations30 Sep 2015 Michiel Hermans, Thomas Van Vaerenbergh

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.

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