Search Results for author:
Found 9 papers, 7 papers with code
Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks
Our findings suggest a new training paradigm for photonics-accelerated artificial intelligence based entirely on a physical analog of the popular backpropagation technique.
Inverse design of photonic crystals through automatic differentiation
Here, we overcome this through the use of automatic differentiation, which is a generalization of the adjoint variable method to arbitrary computational graphs.
Optics Computational Engineering, Finance, and Science Applied Physics Computational Physics
Parallel fault-tolerant programming of an arbitrary feedforward photonic network
Reconfigurable photonic mesh networks of tunable beamsplitter nodes can linearly transform $N$-dimensional vectors representing input modal amplitudes of light for applications such as energy-efficient machine learning hardware, quantum information processing, and mode demultiplexing.
Forward-Mode Differentiation of Maxwell's Equations
We present a previously unexplored forward-mode differentiation method for Maxwell's equations, with applications in the field of sensitivity analysis.
Optics Numerical Analysis Numerical Analysis Computational Physics
Wave Physics as an Analog Recurrent Neural Network
These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.
Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks.
Reconfigurable Photonic Circuit for Controlled Power Delivery to Laser-Driven Accelerators on a Chip
Dielectric laser acceleration (DLA) represents a promising approach to building miniature particle accelerators on a chip.
Optics Accelerator Physics Applied Physics
Adjoint method and inverse design for nonlinear nanophotonic devices
Here, we present an extension of this method to modeling nonlinear devices in the frequency domain, with the nonlinear response directly included in the gradient computation.
Optics Applied Physics Computational Physics
Training of photonic neural networks through in situ backpropagation
Recently, integrated optics has gained interest as a hardware platform for implementing machine learning algorithms.
