R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).

[Crossref]

D. Zibar, A. M. R. Brusin, U. C. de Moura, F. D. Ros, V. Curri, and A. Carena, “Inverse system design using machine learning: the Raman amplifier case,” J. Lightwave Technol. 38(4), 736–753 (2020).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

B. Hu, B. Wu, D. Tan, J. Xu, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27(25), 36276–36285 (2019).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

N. A. Ahmad, “A globally convergent stochastic pairwise conjugate gradient-based algorithm for adaptive filtering,” IEEE Signal Process. Lett. 15, 914–917 (2008).

[Crossref]

J. Robinson and Y. R. Samii, “Particle swarm optimization in electromagnetics,” IEEE Trans. Antennas Propag. 52(2), 397–407 (2004).

[Crossref]

W. Li, X. Li, and W. P. Huang, “A traveling-wave model of laser diodes with consideration for thermal effects,” Opt. Quantum Electron. 36(8), 709–724 (2004).

[Crossref]

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Networks 4, 1942–1948 (1995).

[Crossref]

S. W. Piche, “Steepest descent algorithms for neural network controllers and filters,” IEEE Trans. Neural Netw. 5(2), 198–212 (1994).

[Crossref]

M. G. Davis and R. F. O’Dowd, “A transfer matrix method based large-signal dynamic model for multielectrode DFB lasers,” IEEE J. Quantum Electron. 30(11), 2458–2466 (1994).

[Crossref]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680 (1983).

[Crossref]

N. A. Ahmad, “A globally convergent stochastic pairwise conjugate gradient-based algorithm for adaptive filtering,” IEEE Signal Process. Lett. 15, 914–917 (2008).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

M. G. Davis and R. F. O’Dowd, “A transfer matrix method based large-signal dynamic model for multielectrode DFB lasers,” IEEE J. Quantum Electron. 30(11), 2458–2466 (1994).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Networks 4, 1942–1948 (1995).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680 (1983).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

Y. Li, Y. P. Xi, X. Li, and W. P. Huang, “Design and analysis of single mode Fabry-Perot lasers with high speed modulation capability,” Opt. Express 19(13), 12131–12140 (2011).

[Crossref]

W. Li, X. Li, and W. P. Huang, “A traveling-wave model of laser diodes with consideration for thermal effects,” Opt. Quantum Electron. 36(8), 709–724 (2004).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Networks 4, 1942–1948 (1995).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680 (1983).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

W. Li, X. Li, and W. P. Huang, “A traveling-wave model of laser diodes with consideration for thermal effects,” Opt. Quantum Electron. 36(8), 709–724 (2004).

[Crossref]

Y. Li, Y. P. Xi, X. Li, and W. P. Huang, “Design and analysis of single mode Fabry-Perot lasers with high speed modulation capability,” Opt. Express 19(13), 12131–12140 (2011).

[Crossref]

W. Li, X. Li, and W. P. Huang, “A traveling-wave model of laser diodes with consideration for thermal effects,” Opt. Quantum Electron. 36(8), 709–724 (2004).

[Crossref]

X. Li, Optoelectronic devices: design, modeling, and simulation. (Cambridge University, 2009).

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

Y. Li, Y. P. Xi, X. Li, and W. P. Huang, “Design and analysis of single mode Fabry-Perot lasers with high speed modulation capability,” Opt. Express 19(13), 12131–12140 (2011).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

M. G. Davis and R. F. O’Dowd, “A transfer matrix method based large-signal dynamic model for multielectrode DFB lasers,” IEEE J. Quantum Electron. 30(11), 2458–2466 (1994).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

S. W. Piche, “Steepest descent algorithms for neural network controllers and filters,” IEEE Trans. Neural Netw. 5(2), 198–212 (1994).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

J. Robinson and Y. R. Samii, “Particle swarm optimization in electromagnetics,” IEEE Trans. Antennas Propag. 52(2), 397–407 (2004).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

J. Robinson and Y. R. Samii, “Particle swarm optimization in electromagnetics,” IEEE Trans. Antennas Propag. 52(2), 397–407 (2004).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

S. F. Shu, “Evolving ultrafast laser information by a learning genetic algorithm combined with a knowledge base,” IEEE Photonics Technol. Lett. 18(2), 379–381 (2006).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680 (1983).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

P. Yeh, Optical waves in layered media (Wiley, 1988).

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training deep neural networks for the inverse design of nanophotonic structures,” ACS Photonics 5(4), 1365–1369 (2018).

[Crossref]

Y. Long, J. Ren, Y. Li, and H. Chen, “Inverse design of photonic topological state via machine learning,” Appl. Phys. Lett. 114(18), 181105 (2019).

[Crossref]

M. G. Davis and R. F. O’Dowd, “A transfer matrix method based large-signal dynamic model for multielectrode DFB lasers,” IEEE J. Quantum Electron. 30(11), 2458–2466 (1994).

[Crossref]

R. S. Hegde, “Photonics inverse design: pairing deep neural networks with evolutionary algorithms,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).

[Crossref]

P. H. Fu, T. Y. Huang, K. W. Fan, and D. W. Huang, “Optimization for ultrabroadband polarization beam splitters using a genetic algorithm,” IEEE Photonics J. 11(1), 1–11 (2019).

[Crossref]

S. F. Shu, “Evolving ultrafast laser information by a learning genetic algorithm combined with a knowledge base,” IEEE Photonics Technol. Lett. 18(2), 379–381 (2006).

[Crossref]

N. A. Ahmad, “A globally convergent stochastic pairwise conjugate gradient-based algorithm for adaptive filtering,” IEEE Signal Process. Lett. 15, 914–917 (2008).

[Crossref]

J. Robinson and Y. R. Samii, “Particle swarm optimization in electromagnetics,” IEEE Trans. Antennas Propag. 52(2), 397–407 (2004).

[Crossref]

S. W. Piche, “Steepest descent algorithms for neural network controllers and filters,” IEEE Trans. Neural Netw. 5(2), 198–212 (1994).

[Crossref]

Z. C. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, “Generative model for the inverse design of metasurfaces,” Nano Lett. 18(10), 6570–6576 (2018).

[Crossref]

D. Melati, Y. Grinberg, M. K. Dezfouli, S. Janz, P. Cheben, J. H. Schmid, A. Sanchez-Postigo, and D. X. Xu, “Mapping the global design space of nanophotonic components using machine learning pattern recognition,” Nat. Commun. 10(1), 4775 (2019).

[Crossref]

G. P. P. Pun, R. Batra, R. Ramprasad, and Y. Mishin, “Physically informed artificial neural networks for atomistic modeling of materials,” Nat. Commun. 10(1), 2339 (2019).

[Crossref]

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).

[Crossref]

B. Hu, B. Wu, D. Tan, J. Xu, and Y. Chen, “Robust inverse-design of scattering spectrum in core-shell structure using modified denoising autoencoder neural network,” Opt. Express 27(25), 36276–36285 (2019).

[Crossref]

Y. Li, Y. P. Xi, X. Li, and W. P. Huang, “Design and analysis of single mode Fabry-Perot lasers with high speed modulation capability,” Opt. Express 19(13), 12131–12140 (2011).

[Crossref]

W. Li, X. Li, and W. P. Huang, “A traveling-wave model of laser diodes with consideration for thermal effects,” Opt. Quantum Electron. 36(8), 709–724 (2004).

[Crossref]

J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. of IEEE Int. Conf. on Neural Networks 4, 1942–1948 (1995).

[Crossref]

J. Peurifoy, Y. C. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljacic, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 4(6), eaar4206 (2018).

[Crossref]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680 (1983).

[Crossref]

P. Yeh, Optical waves in layered media (Wiley, 1988).

X. Li, Optoelectronic devices: design, modeling, and simulation. (Cambridge University, 2009).