Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light-matter interactions in nanophotonic devices,” arXiv preprint arXiv:1905.06889 (2019).

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light-matter interactions in nanophotonic devices,” arXiv preprint arXiv:1905.06889 (2019).

M. R. Karim, H. Ahmad, and B. M. A. Rahman, “All-normal dispersion chalcogenide PCF for ultraflat mid-infrared supercontinuum generation,” IEEE Photonics Technol. Lett. 29(21), 1792–1795 (2017).

[Crossref]

A. Tittl, A. John-Herpin, A. Leitis, E. R. Arvelo, and H. Altug, “Metasurface-based molecular biosensing aided by artificial intelligence,” Angewandte Chemie Int. Ed. 58(42), 14810–14822 (2019).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

F. Benabid, J. C. Knight, G. Antonopoulos, and P. St. J. Russell, “Stimulated Raman scattering in hydrogen-filled hollow-core photonic crystal fiber,” Science 298(5592), 399–402 (2002).

[Crossref]

A. Tittl, A. John-Herpin, A. Leitis, E. R. Arvelo, and H. Altug, “Metasurface-based molecular biosensing aided by artificial intelligence,” Angewandte Chemie Int. Ed. 58(42), 14810–14822 (2019).

[Crossref]

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

F. Benabid, G. Bouwmans, J. C. Knight, P. St. J. Russell, and F. Couny, “Ultrahigh efficiency laser wavelength conversion in a gas-filled hollow core photonic crystal fiber by pure stimulated rotational Raman scattering in molecular hydrogen,” Phys. Rev. Lett. 93(12), 123903 (2004).

[Crossref]

F. Benabid, J. C. Knight, G. Antonopoulos, and P. St. J. Russell, “Stimulated Raman scattering in hydrogen-filled hollow-core photonic crystal fiber,” Science 298(5592), 399–402 (2002).

[Crossref]

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

W. J. Wadsworth, A. Ortigosa-Blanch, J. C. Knight, T. A. Birks, T.-P. M. Man, and P. St. J. Russell, “Supercontinuum generation in photonic crystal fibers and optical fiber tapers: a novel light source,” J. Opt. Soc. Am. B 19(9), 2148–2155 (2002).

[Crossref]

J. C. Knight, T. A. Birks, P. St. J. Russell, and D. M. Atkin, “All-silica single-mode optical fiber with photonic crystal cladding,” Opt. Lett. 21(19), 1547–1549 (1996).

[Crossref]

F. Benabid, G. Bouwmans, J. C. Knight, P. St. J. Russell, and F. Couny, “Ultrahigh efficiency laser wavelength conversion in a gas-filled hollow core photonic crystal fiber by pure stimulated rotational Raman scattering in molecular hydrogen,” Phys. Rev. Lett. 93(12), 123903 (2004).

[Crossref]

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

C.-P. Yu and H.-C. Chang, “Applications of the finite difference mode solution method to photonic crystal structures,” Opt. Quantum Electron. 36(1-3), 145–163 (2004).

[Crossref]

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).

[Crossref]

P. K. Cheo, A. Liu, and G. G. King, “A high-brightness laser beam from a phase-locked multicore Yb-doped fiber laser array,” IEEE Photonics Technol. Lett. 13(5), 439–441 (2001).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

S. Chugh, S. Ghosh, A. Gulistan, and B. M. A. Rahman, “Machine learning regression approach to the nanophotonic waveguide analyses,” http://dx.doi.org/10.1109/JLT.2019.2946572 (2019).

F. Benabid, G. Bouwmans, J. C. Knight, P. St. J. Russell, and F. Couny, “Ultrahigh efficiency laser wavelength conversion in a gas-filled hollow core photonic crystal fiber by pure stimulated rotational Raman scattering in molecular hydrogen,” Phys. Rev. Lett. 93(12), 123903 (2004).

[Crossref]

A. Cucinotta, S. Selleri, L. Vincetti, and M. Zoboli, “Holey fiber analysis through the finite-element method,” IEEE Photonics Technol. Lett. 14(11), 1530–1532 (2002).

[Crossref]

M. De, T. K. Gangopadhyay, and V. K. Singh, “Prospects of photonic crystal fiber as physical sensor: An overview,” Sensors 19(3), 464 (2019).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

M. De, T. K. Gangopadhyay, and V. K. Singh, “Prospects of photonic crystal fiber as physical sensor: An overview,” Sensors 19(3), 464 (2019).

[Crossref]

S. Chugh, S. Ghosh, A. Gulistan, and B. M. A. Rahman, “Machine learning regression approach to the nanophotonic waveguide analyses,” http://dx.doi.org/10.1109/JLT.2019.2946572 (2019).

S. S. A. Obayya, B. M. A. Rahman, and K. T. V. Grattan, “Accurate finite element modal solution of photonic crystal fibres,” IEE Proc.: Optoelectron. 152(5), 241–246 (2005).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

S. Chugh, S. Ghosh, A. Gulistan, and B. M. A. Rahman, “Machine learning regression approach to the nanophotonic waveguide analyses,” http://dx.doi.org/10.1109/JLT.2019.2946572 (2019).

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light-matter interactions in nanophotonic devices,” arXiv preprint arXiv:1905.06889 (2019).

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

Y. LeCun, D. Touresky, G. Hinton, and T. Sejnowski, “A theoretical framework for back-propagation,” in Proceedings of the 1988 Connectionist Models Summer School, vol. 1 (CMU, Pittsburgh, Pa: Morgan Kaufmann, 1988), pp. 21–28.

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

A. Tittl, A. John-Herpin, A. Leitis, E. R. Arvelo, and H. Altug, “Metasurface-based molecular biosensing aided by artificial intelligence,” Angewandte Chemie Int. Ed. 58(42), 14810–14822 (2019).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

M. R. Karim, H. Ahmad, and B. M. A. Rahman, “All-normal dispersion chalcogenide PCF for ultraflat mid-infrared supercontinuum generation,” IEEE Photonics Technol. Lett. 29(21), 1792–1795 (2017).

[Crossref]

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light-matter interactions in nanophotonic devices,” arXiv preprint arXiv:1905.06889 (2019).

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

P. K. Cheo, A. Liu, and G. G. King, “A high-brightness laser beam from a phase-locked multicore Yb-doped fiber laser array,” IEEE Photonics Technol. Lett. 13(5), 439–441 (2001).

[Crossref]

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

F. Benabid, G. Bouwmans, J. C. Knight, P. St. J. Russell, and F. Couny, “Ultrahigh efficiency laser wavelength conversion in a gas-filled hollow core photonic crystal fiber by pure stimulated rotational Raman scattering in molecular hydrogen,” Phys. Rev. Lett. 93(12), 123903 (2004).

[Crossref]

F. Benabid, J. C. Knight, G. Antonopoulos, and P. St. J. Russell, “Stimulated Raman scattering in hydrogen-filled hollow-core photonic crystal fiber,” Science 298(5592), 399–402 (2002).

[Crossref]

W. J. Wadsworth, A. Ortigosa-Blanch, J. C. Knight, T. A. Birks, T.-P. M. Man, and P. St. J. Russell, “Supercontinuum generation in photonic crystal fibers and optical fiber tapers: a novel light source,” J. Opt. Soc. Am. B 19(9), 2148–2155 (2002).

[Crossref]

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

J. C. Knight, T. A. Birks, P. St. J. Russell, and D. M. Atkin, “All-silica single-mode optical fiber with photonic crystal cladding,” Opt. Lett. 21(19), 1547–1549 (1996).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

Y. LeCun, D. Touresky, G. Hinton, and T. Sejnowski, “A theoretical framework for back-propagation,” in Proceedings of the 1988 Connectionist Models Summer School, vol. 1 (CMU, Pittsburgh, Pa: Morgan Kaufmann, 1988), pp. 21–28.

A. Tittl, A. John-Herpin, A. Leitis, E. R. Arvelo, and H. Altug, “Metasurface-based molecular biosensing aided by artificial intelligence,” Angewandte Chemie Int. Ed. 58(42), 14810–14822 (2019).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

P. K. Cheo, A. Liu, and G. G. King, “A high-brightness laser beam from a phase-locked multicore Yb-doped fiber laser array,” IEEE Photonics Technol. Lett. 13(5), 439–441 (2001).

[Crossref]

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).

[Crossref]

W. Ma, F. Cheng, and Y. Liu, “Deep-learning-enabled on-demand design of chiral metamaterials,” ACS Nano 12(6), 6326–6334 (2018).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

R. A. Norton and R. Scheichl, “Planewave expansion methods for photonic crystal fibres,” Appl. Numer. Math. 63, 88–104 (2013).

[Crossref]

S. S. A. Obayya, B. M. A. Rahman, and K. T. V. Grattan, “Accurate finite element modal solution of photonic crystal fibres,” IEE Proc.: Optoelectron. 152(5), 241–246 (2005).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

M. R. Karim, H. Ahmad, and B. M. A. Rahman, “All-normal dispersion chalcogenide PCF for ultraflat mid-infrared supercontinuum generation,” IEEE Photonics Technol. Lett. 29(21), 1792–1795 (2017).

[Crossref]

S. S. A. Obayya, B. M. A. Rahman, and K. T. V. Grattan, “Accurate finite element modal solution of photonic crystal fibres,” IEE Proc.: Optoelectron. 152(5), 241–246 (2005).

[Crossref]

S. Chugh, S. Ghosh, A. Gulistan, and B. M. A. Rahman, “Machine learning regression approach to the nanophotonic waveguide analyses,” http://dx.doi.org/10.1109/JLT.2019.2946572 (2019).

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

T. M. Monro, W. Belardi, K. Furusawa, J. C. Baggett, N. G. R. Broderick, and D. J. Richardson, “Sensing with microstructured optical fibres,” Meas. Sci. Technol. 12(7), 854–858 (2001).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

F. Benabid, G. Bouwmans, J. C. Knight, P. St. J. Russell, and F. Couny, “Ultrahigh efficiency laser wavelength conversion in a gas-filled hollow core photonic crystal fiber by pure stimulated rotational Raman scattering in molecular hydrogen,” Phys. Rev. Lett. 93(12), 123903 (2004).

[Crossref]

F. Benabid, J. C. Knight, G. Antonopoulos, and P. St. J. Russell, “Stimulated Raman scattering in hydrogen-filled hollow-core photonic crystal fiber,” Science 298(5592), 399–402 (2002).

[Crossref]

W. J. Wadsworth, A. Ortigosa-Blanch, J. C. Knight, T. A. Birks, T.-P. M. Man, and P. St. J. Russell, “Supercontinuum generation in photonic crystal fibers and optical fiber tapers: a novel light source,” J. Opt. Soc. Am. B 19(9), 2148–2155 (2002).

[Crossref]

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

J. C. Knight, T. A. Birks, P. St. J. Russell, and D. M. Atkin, “All-silica single-mode optical fiber with photonic crystal cladding,” Opt. Lett. 21(19), 1547–1549 (1996).

[Crossref]

P. St. J. Russell, “Photonic crystal fibers: Basics and applications,” in Optical Fiber Telecommunications VA, (Elsevier, 2008), pp. 485–522.

R. A. Norton and R. Scheichl, “Planewave expansion methods for photonic crystal fibres,” Appl. Numer. Math. 63, 88–104 (2013).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

Y. LeCun, D. Touresky, G. Hinton, and T. Sejnowski, “A theoretical framework for back-propagation,” in Proceedings of the 1988 Connectionist Models Summer School, vol. 1 (CMU, Pittsburgh, Pa: Morgan Kaufmann, 1988), pp. 21–28.

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

A. Cucinotta, S. Selleri, L. Vincetti, and M. Zoboli, “Holey fiber analysis through the finite-element method,” IEEE Photonics Technol. Lett. 14(11), 1530–1532 (2002).

[Crossref]

M. De, T. K. Gangopadhyay, and V. K. Singh, “Prospects of photonic crystal fiber as physical sensor: An overview,” Sensors 19(3), 464 (2019).

[Crossref]

B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” J. Lightwave Technol. 36(20), 4843–4855 (2018).

[Crossref]

A. Tittl, A. John-Herpin, A. Leitis, E. R. Arvelo, and H. Altug, “Metasurface-based molecular biosensing aided by artificial intelligence,” Angewandte Chemie Int. Ed. 58(42), 14810–14822 (2019).

[Crossref]

F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore, “An overview on application of machine learning techniques in optical networks,” IEEE Commun. Surv. Tutorials 21(2), 1383–1408 (2019).

[Crossref]

Y. LeCun, D. Touresky, G. Hinton, and T. Sejnowski, “A theoretical framework for back-propagation,” in Proceedings of the 1988 Connectionist Models Summer School, vol. 1 (CMU, Pittsburgh, Pa: Morgan Kaufmann, 1988), pp. 21–28.

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

A. Cucinotta, S. Selleri, L. Vincetti, and M. Zoboli, “Holey fiber analysis through the finite-element method,” IEEE Photonics Technol. Lett. 14(11), 1530–1532 (2002).

[Crossref]

W. J. Wadsworth, A. Ortigosa-Blanch, J. C. Knight, T. A. Birks, T.-P. M. Man, and P. St. J. Russell, “Supercontinuum generation in photonic crystal fibers and optical fiber tapers: a novel light source,” J. Opt. Soc. Am. B 19(9), 2148–2155 (2002).

[Crossref]

R. Holzwarth, Th. Udem, T. W. Hänsch, J. C. Knight, W. J. Wadsworth, and P. St. J. Russell, “Optical frequency synthesizer for precision spectroscopy,” Phys. Rev. Lett. 85(11), 2264–2267 (2000).

[Crossref]

J. Baxter, A. C. Lesina, J.-M. Guay, A. Weck, P. Berini, and L. Ramunno, “Plasmonic colours predicted by deep learning,” Sci. Rep. 9(1), 8074 (2019).

[Crossref]

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in PyTorch,” in NIPS Autodiff Workshop, (2017).

J. Jiang, D. Sell, S. Hoyer, J. Hickey, J. Yang, and J. A. Fan, “Free-form diffractive metagrating design based on generative adversarial networks,” ACS Nano 13(8), 8872–8878 (2019).

[Crossref]

C.-P. Yu and H.-C. Chang, “Applications of the finite difference mode solution method to photonic crystal structures,” Opt. Quantum Electron. 36(1-3), 145–163 (2004).

[Crossref]

Y. Kiarashinejad, M. Zandehshahvar, S. Abdollahramezani, O. Hemmatyar, R. Pourabolghasem, and A. Adibi, “Knowledge discovery in nanophotonics using geometric deep learning,” arXiv preprint arXiv:1909.07330 (2019).

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