The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures
Open accessAbstract
Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of nonuniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain nonunique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that require large training data sets.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Flat optics with designer metasurfaces
Yu, N., & Capasso, F. (2014) · Nature Materials · doi:10.1038/nmat3839 · Cited by 5,984
Spin Glass Theory and Beyond: An Introduction to the Replica Method and Its Applications
Mezard, M., Parisi, G., & Virasoro, M. (1986) · World Scientific Lecture Notes in Physics · doi:10.1142/0271 · Cited by 3,653
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
Cited by
Nanophotonic particle simulation and inverse design using artificial neural networks
Peurifoy, J., Shen, Y., Jing, L., Yang, Y., Cano-Renteria, F., DeLacy, B. G., Joannopoulos, J. D., Tegmark, M., & Soljačić, M. (2018) · Science Advances · doi:10.1126/sciadv.aar4206 · Cited by 964
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
Ma, W., Cheng, F., & Liu, Y. (2018) · ACS Nano · doi:10.1021/acsnano.8b03569 · Cited by 925
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 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
Metadata
- Authors
- Liu, D., Tan, Y., Khoram, E., & Yu, Z.
- Venue
- ACS Photonics
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-02-25
- Volume
- 5
- Issue
- 4
- Pages
- 1365-1369
- ISSN
- 2330-4022 (ISSN-L)
- OpenAlex
- W2766162919
- Cited by
- 1,181
- References
- 4
- FWCI
- 25.40
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
