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Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch

Floris Laporte (UGent) , Joni Dambre (UGent) and Peter Bienstman (UGent)
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Abstract
We propose a new method for performing photonic circuit simulations based on the scatter matrix formalism. We leverage the popular deep-learning framework PyTorch to reimagine photonic circuits as sparsely connected complex-valued neural networks. This allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation.
Keywords
Multidisciplinary, COUPLED-MODE THEORY

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Citation

Please use this url to cite or link to this publication:

MLA
Laporte, Floris, et al. “Highly Parallel Simulation and Optimization of Photonic Circuits in Time and Frequency Domain Based on the Deep-Learning Framework PyTorch.” SCIENTIFIC REPORTS, vol. 9, 2019.
APA
Laporte, F., Dambre, J., & Bienstman, P. (2019). Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch. SCIENTIFIC REPORTS, 9.
Chicago author-date
Laporte, Floris, Joni Dambre, and Peter Bienstman. 2019. “Highly Parallel Simulation and Optimization of Photonic Circuits in Time and Frequency Domain Based on the Deep-Learning Framework PyTorch.” SCIENTIFIC REPORTS 9.
Chicago author-date (all authors)
Laporte, Floris, Joni Dambre, and Peter Bienstman. 2019. “Highly Parallel Simulation and Optimization of Photonic Circuits in Time and Frequency Domain Based on the Deep-Learning Framework PyTorch.” SCIENTIFIC REPORTS 9.
Vancouver
1.
Laporte F, Dambre J, Bienstman P. Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch. SCIENTIFIC REPORTS. 2019;9.
IEEE
[1]
F. Laporte, J. Dambre, and P. Bienstman, “Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch,” SCIENTIFIC REPORTS, vol. 9, 2019.
@article{8626263,
  abstract     = {We propose a new method for performing photonic circuit simulations based on the scatter matrix formalism. We leverage the popular deep-learning framework PyTorch to reimagine photonic circuits as sparsely connected complex-valued neural networks. This allows for highly parallel simulation of large photonic circuits on graphical processing units in time and frequency domain while all parameters of each individual component can easily be optimized with well-established machine learning algorithms such as backpropagation.},
  articleno    = {5918},
  author       = {Laporte, Floris and Dambre, Joni and Bienstman, Peter},
  issn         = {2045-2322},
  journal      = {SCIENTIFIC REPORTS},
  keywords     = {Multidisciplinary,COUPLED-MODE THEORY},
  language     = {eng},
  pages        = {9},
  title        = {Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch},
  url          = {http://dx.doi.org/10.1038/s41598-019-42408-2},
  volume       = {9},
  year         = {2019},
}

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