Scientific Computing with Diffractive Optical Neural Networks

Abstract

We deploy reconfigurable diffractive optical neural networks for multiple scientific computing applications, including guiding quantum material synthesis, predicting properties of materials, biomolecules, and nanophotonic devices, and dynamic stabilization of an inverted pendulum with reinforcement learning.