The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Nanophotonic particle simulation and inverse design using artificial neural networks
Open accessAbstract
New deep learning techniques may hold the key to solving intractable photonics problems.
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Cites
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Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Absorption and Scattering of Light by Small Particles
Bohren, C. F., & Huffman, D. R. (1998) · doi:10.1002/9783527618156 · Cited by 18,474
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Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
Solving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures
Liu, D., Tan, Y., Khoram, E., & Yu, Z. (2018) · ACS Photonics · doi:10.1021/acsphotonics.7b01377 · Cited by 1,181
Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
Piggott, A. Y., Lu, J., Lagoudakis, K. G., Petykiewicz, J., Babinec, T. M., & Vučković, J. (2015) · Nature Photonics · doi:10.1038/nphoton.2015.69 · Cited by 1,113
An Introduction to the Adjoint Approach to Design
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Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries
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Cited by
Deep-learning-enabled self-adaptive microwave cloak without human intervention
Qian, C., Zheng, B., Shen, Y., Jing, L., Li, E., Shen, L., & Chen, H. (2020) · Nature Photonics · doi:10.1038/s41566-020-0604-2 · Cited by 585
Self-citationDeep learning enabled inverse design in nanophotonics
So, S., Badloe, T., Noh, J., Rho, J., & Bravo‐Abad, J. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0474 · Cited by 564
Review of numerical optimization techniques for meta-device design [Invited]
Campbell, S. D., Sell, D., Jenkins, R. P., Whiting, E. B., Fan, J. A., & Werner, D. H. (2019) · Optical Materials Express · doi:10.1364/ome.9.001842 · Cited by 322
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311
Machine Learning in Nanoscience: Big Data at Small Scales
Brown, K. A., Brittman, S., Maccaferri, N., Jariwala, D., & Celano, U. (2020) · Nano Letters · doi:10.1021/acs.nanolett.9b04090 · Cited by 229
Metadata
- Authors
- Peurifoy, J., Shen, Y., Jing, L., Yang, Y., Cano-Renteria, F., DeLacy, B. G., Joannopoulos, J. D., Tegmark, M., & Soljačić, M.
- Venue
- Science Advances
- Published
- 2018-06
- Volume
- 4
- Issue
- 6
- Pages
- eaar4206-eaar4206
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W2775280502
- Cited by
- 964
- References
- 16
- FWCI
- 25.18
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Nanophotonic particle simulation and inverse design using artificial neural networks. https://app.cochat.ai/papers/10.1126/sciadv.aar4206
- The paper
- Peurifoy, J., Shen, Y., Jing, L., Yang, Y., Cano-Renteria, F., DeLacy, B. G., Joannopoulos, J. D., Tegmark, M., & Soljačić, M. (2018). Nanophotonic particle simulation and inverse design using artificial neural networks. Science Advances. https://doi.org/10.1126/sciadv.aar4206
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
