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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Citations
Cites
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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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Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
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Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling
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Inverse Design of High Absorption Thin‐Film Photovoltaic Materials
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Interior-point methods for optimization
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Nanophotonic Inverse Design Using Artificial Neural Network
Peurifoy, J., Shen, Y., Yang, Y., Jing, L., Cano-Renteria, F., Joannopoulos, J. D., Tegmark, M., & Marin Soljacic, A. (2017) · Frontiers in Optics · doi:10.1364/fio.2017.fth4a.4 · Cited by 14
Self-citation
Cited by
Physics-Informed Neural Networks with Hard Constraints for Inverse Design
Lu, L., Pestourie, R., Yao, W., Wang, Z., Verdugo, F., & Johnson, S. G. (2021) · SIAM Journal on Scientific Computing · doi:10.1137/21m1397908 · Cited by 771
Ma, W., Cheng, F., Xu, Y., Wen, Q., & Liu, Y. (2019) · Advanced Materials · doi:10.1002/adma.201901111 · Cited by 615
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
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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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.
