D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref]
[PubMed]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).

C. Chen and S. Li, “Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles,” ACS Photonics 5, 2295–2304 (2018).

[Crossref]

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref]
[PubMed]

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Contr. Sign. Syst. 2, 303–314 (1989).

[Crossref]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” arXiv preprint arXiv:1603.07285 (2016).

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

J. Y. Suen, K. Fan, and W. J. Padilla, “A Zero-Rank, Maximum Nullity Perfect Electromagnetic Wave Absorber,” Adv. Opt. Mater. 1801632, 1–6 (2019).

X. Liu, K. Fan, I. V. Shadrivov, and W. J. Padilla, “Experimental realization of a terahertz all-dielectric metasurface absorber,” Opt. Express 25, 191–201 (2017).

[Crossref]
[PubMed]

K. Fan, J. Y. Suen, X. Liu, and W. J. Padilla, “All-dielectric metasurface absorbers for uncooled terahertz imaging,” Optica 4, 601–604 (2017).

[Crossref]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref]
[PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks 2, 359–366 (1989).

[Crossref]

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

I. Sajedian, J. Kim, and J. Rho, “Predicting resonant properties of plasmonic structures by deep learning,” arXiv preprint arXiv:1805.00312 (2018).

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref]
[PubMed]

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).

C. Chen and S. Li, “Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles,” ACS Photonics 5, 2295–2304 (2018).

[Crossref]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

H. W. Lin, M. Tegmark, and D. Rolnick, “Why does deep and cheap learning work so well?” J. Stat. Phys. 168, 1223–1247 (2017).

[Crossref]

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

X. Liu, K. Fan, I. V. Shadrivov, and W. J. Padilla, “Experimental realization of a terahertz all-dielectric metasurface absorber,” Opt. Express 25, 191–201 (2017).

[Crossref]
[PubMed]

K. Fan, J. Y. Suen, X. Liu, and W. J. Padilla, “All-dielectric metasurface absorbers for uncooled terahertz imaging,” Optica 4, 601–604 (2017).

[Crossref]

X. Ming, X. Liu, L. Sun, and W. J. Padilla, “Degenerate critical coupling in all-dielectric metasurface absorbers,” Opt. Express 25, 24658 (2017).

[Crossref]
[PubMed]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref]
[PubMed]

M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Trans. Pattern Analysis Mach. Intell. 36, 2227–2240 (2014).

[Crossref]

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref]
[PubMed]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Trans. Pattern Analysis Mach. Intell. 36, 2227–2240 (2014).

[Crossref]

J. L. Müller, Linear and Nonlinear Inverse Problems with Practical Applications (Computational Science and Engineering) (Society for Industrial and Applied Mathematics, 2012).

[Crossref]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

J. Y. Suen, K. Fan, and W. J. Padilla, “A Zero-Rank, Maximum Nullity Perfect Electromagnetic Wave Absorber,” Adv. Opt. Mater. 1801632, 1–6 (2019).

X. Liu, K. Fan, I. V. Shadrivov, and W. J. Padilla, “Experimental realization of a terahertz all-dielectric metasurface absorber,” Opt. Express 25, 191–201 (2017).

[Crossref]
[PubMed]

K. Fan, J. Y. Suen, X. Liu, and W. J. Padilla, “All-dielectric metasurface absorbers for uncooled terahertz imaging,” Optica 4, 601–604 (2017).

[Crossref]

X. Ming, X. Liu, L. Sun, and W. J. Padilla, “Degenerate critical coupling in all-dielectric metasurface absorbers,” Opt. Express 25, 24658 (2017).

[Crossref]
[PubMed]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref]

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref]

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

I. Sajedian, J. Kim, and J. Rho, “Predicting resonant properties of plasmonic structures by deep learning,” arXiv preprint arXiv:1805.00312 (2018).

H. W. Lin, M. Tegmark, and D. Rolnick, “Why does deep and cheap learning work so well?” J. Stat. Phys. 168, 1223–1247 (2017).

[Crossref]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

I. Sajedian, J. Kim, and J. Rho, “Predicting resonant properties of plasmonic structures by deep learning,” arXiv preprint arXiv:1805.00312 (2018).

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (2019).

[Crossref]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks 2, 359–366 (1989).

[Crossref]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

J. Y. Suen, K. Fan, and W. J. Padilla, “A Zero-Rank, Maximum Nullity Perfect Electromagnetic Wave Absorber,” Adv. Opt. Mater. 1801632, 1–6 (2019).

K. Fan, J. Y. Suen, X. Liu, and W. J. Padilla, “All-dielectric metasurface absorbers for uncooled terahertz imaging,” Optica 4, 601–604 (2017).

[Crossref]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

H. W. Lin, M. Tegmark, and D. Rolnick, “Why does deep and cheap learning work so well?” J. Stat. Phys. 168, 1223–1247 (2017).

[Crossref]

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” arXiv preprint arXiv:1603.07285 (2016).

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks 2, 359–366 (1989).

[Crossref]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

D. Liu, Y. Tan, E. Khoram, and Z. Yu, “Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures,” ACS Photonics 5, 1365–1369 (2018).

[Crossref]

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

M. Ziatdinov, O. Dyck, A. Maksov, X. Li, X. Sang, K. Xiao, R. R. Unocic, R. Vasudevan, S. Jesse, and S. V. Kalinin, “Deep learning of atomically resolved scanning transmission electron microscopy images: chemical identification and tracking local transformations,” ACS Nano 11, 12742–12752 (2017).

[Crossref]
[PubMed]

F. Lussier, D. Missirlis, J. P. Spatz, and J.-F. Masson, “Machine learning driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells,” ACS Nano 13, 1403–1411 (2019).

[PubMed]

S. Sun, Z. Zhou, C. Zhang, Y. Gao, Z. Duan, S. Xiao, and Q. Song, “All-dielectric full-color printing with tio2 metasurfaces,” ACS Nano 11, 4445–4452 (2017). PMID: .

[Crossref]
[PubMed]

J. Sautter, I. Staude, M. Decker, E. Rusak, D. N. Neshev, I. Brener, and Y. S. Kivshar, “Active tuning of all-dielectric metasurfaces,” ACS Nano 9, 4308–4315 (2015).

[Crossref]
[PubMed]

W. Ma, F. Cheng, and Y. Liu, “Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials,” ACS Nano 12, 6326–6334 (2018).

[Crossref]
[PubMed]

C. Chen and S. Li, “Valence electron density-dependent pseudopermittivity for nonlocal effects in optical properties of metallic nanoparticles,” ACS Photonics 5, 2295–2304 (2018).

[Crossref]

Y. Qu, L. Jing, Y. Shen, M. Qiu, and M. Soljačić, “Migrating knowledge between physical scenarios based on artificial neural networks,” ACS Photonics 6, 1168–1174 (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, 1365–1369 (2018).

[Crossref]

D. Headland, E. Carrasco, S. Nirantar, W. Withayachumnankul, P. Gutruf, J. Schwarz, D. Abbott, M. Bhaskaran, S. Sriram, J. Perruisseau-Carrier, and C. Fumeaux, “Dielectric resonator reflectarray as high-efficiency nonuniform terahertz metasurface,” ACS Photonics 3, 1019–1026 (2016).

[Crossref]

J. Y. Suen, K. Fan, and W. J. Padilla, “A Zero-Rank, Maximum Nullity Perfect Electromagnetic Wave Absorber,” Adv. Opt. Mater. 1801632, 1–6 (2019).

C. C. Chang, L. Huang, J. Nogan, and H. T. Chen, “Invited Article: Narrowband terahertz bandpass filters employing stacked bilayer metasurface antireflection structures,” APL Photonics 3051602 (2018).

[Crossref]

H. Kabir, Y. Wang, M. Yu, and Q. Zhang, “Neural Network Modeling and Applications to Microwave Design,” IEEE Trans. Microw. Theory Tech. 56, 867 (2008).

[Crossref]

M. Muja and D. G. Lowe, “Scalable nearest neighbor algorithms for high dimensional data,” IEEE Trans. Pattern Analysis Mach. Intell. 36, 2227–2240 (2014).

[Crossref]

H. W. Lin, M. Tegmark, and D. Rolnick, “Why does deep and cheap learning work so well?” J. Stat. Phys. 168, 1223–1247 (2017).

[Crossref]

I. Malkiel, M. Mrejen, A. Nagler, U. Arieli, L. Wolf, and H. Suchowski, “Plasmonic nanostructure design and characterization via Deep Learning,” Light. Sci. Appl. 760 (2018).

[Crossref]

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Contr. Sign. Syst. 2, 303–314 (1989).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436 (2015).

[Crossref]
[PubMed]

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks 2, 359–366 (1989).

[Crossref]

X. Ming, X. Liu, L. Sun, and W. J. Padilla, “Degenerate critical coupling in all-dielectric metasurface absorbers,” Opt. Express 25, 24658 (2017).

[Crossref]
[PubMed]

X. Liu, K. Fan, I. V. Shadrivov, and W. J. Padilla, “Experimental realization of a terahertz all-dielectric metasurface absorber,” Opt. Express 25, 191–201 (2017).

[Crossref]
[PubMed]

X. Liu, T. Tyler, T. Starr, A. F. Starr, N. M. Jokerst, and W. J. Padilla, “Taming the blackbody with infrared metamaterials as selective thermal emitters,” Phys. Rev. Lett. 107, 045901 (2011).

[Crossref]
[PubMed]

J. Peurifoy, Y. Shen, L. Jing, Y. Yang, F. Cano-Renteria, B. G. DeLacy, J. D. Joannopoulos, M. Tegmark, and M. Soljačić, “Nanophotonic particle simulation and inverse design using artificial neural networks,” Sci. Adv. 44206 (2018).

[Crossref]
[PubMed]

Y. Kiarashinejad, S. Abdollahrameazni, and A. Adibi, “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures,” arXiv preprint arXiv:1902.03865 (2019).

I. Sajedian, J. Kim, and J. Rho, “Predicting resonant properties of plasmonic structures by deep learning,” arXiv preprint arXiv:1805.00312 (2018).

Y. Kiarashinejad, S. Abdollahramezani, M. Zandehshahvar, O. Hemmatyar, and A. Adibi, “Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices,” Adv. Theory Simulations0, 1900088.

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” arXiv preprint arXiv:1506.06579 (2015).

M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (IEEE, 2010), pp. 2528–2535.

V. Dumoulin and F. Visin, “A guide to convolution arithmetic for deep learning,” arXiv preprint arXiv:1603.07285 (2016).

J. L. Müller, Linear and Nonlinear Inverse Problems with Practical Applications (Computational Science and Engineering) (Society for Industrial and Applied Mathematics, 2012).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European Conference on Computer Vision, (2016), pp. 21–37.

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” in Advances in Neural Information Processing Systems, (NIPS, 2013), pp. 1–9.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Advances in Neural Information Processing Systems, (NIPS, 2017), Nips.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 (2018).