Search Results for author: Momchil Minkov

Found 8 papers, 6 papers with code

Inverse design of photonic crystals through automatic differentiation

1 code implementation1 Mar 2020 Momchil Minkov, Ian A. D. Williamson, Lucio C. Andreani, Dario Gerace, Beicheng Lou, Alex Y. Song, Tyler W. Hughes, Shanhui Fan

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

1 code implementation11 Sep 2019 Sunil Pai, Ian A. D. Williamson, Tyler W. Hughes, Momchil Minkov, Olav Solgaard, Shanhui Fan, David A. B. Miller

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

2 code implementations28 Aug 2019 Tyler W. Hughes, Ian A. D. Williamson, Momchil Minkov, Shanhui Fan

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

1 code implementation29 Apr 2019 Tyler W. Hughes, Ian A. D. Williamson, Momchil Minkov, Shanhui Fan

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.

BIG-bench Machine Learning Vowel Classification

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

4 code implementations12 Mar 2019 Ian A. D. Williamson, Tyler W. Hughes, Momchil Minkov, Ben Bartlett, Sunil Pai, Shanhui Fan

We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks.

Adjoint method and inverse design for nonlinear nanophotonic devices

1 code implementation3 Nov 2018 Tyler W. Hughes, Momchil Minkov, Ian A. D. Williamson, Shanhui Fan

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

no code implementations25 May 2018 Tyler W. Hughes, Momchil Minkov, Yu Shi, Shanhui Fan

Recently, integrated optics has gained interest as a hardware platform for implementing machine learning algorithms.

BIG-bench Machine Learning

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