The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Wave physics as an analog recurrent neural network
Open accessAbstract
Analog machine learning computations are performed passively by propagating light and sound waves through programmed materials.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
Inverse design in nanophotonics
Molesky, S., Lin, Z., Piggott, A. Y., Jin, W., Vucković, J., & Rodriguez, A. W. (2018) · Nature Photonics · doi:10.1038/s41566-018-0246-9 · Cited by 1,818
Recurrent neural networks and robust time series prediction
Connor, J. T., Martin, R. D., & Atlas, L. E. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279188 · Cited by 1,316
Performing Mathematical Operations with Metamaterials
Silva, A., Monticone, F., Castaldi, G., Galdi, V., Alù, A., & Engheta, N. (2014) · Science · doi:10.1126/science.1242818 · Cited by 1,136
Cited by
Deep learning model to predict complex stress and strain fields in hierarchical composites
Yang, Z., Yu, C.-H., & Buehler, M. J. (2021) · Science Advances · doi:10.1126/sciadv.abd7416 · Cited by 389
Eighty Years of the Finite Element Method: Birth, Evolution, and Future
Liu, W. K., Li, S., & Park, H. S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09740-9 · Cited by 322
Photonic tensor cores for machine learning
Miscuglio, M., & Sorger, V. J. (2020) · Applied Physics Reviews · doi:10.1063/5.0001942 · Cited by 206
Analog Cognition and Consciousness
Miller, E. K., Brincat, S. L., & Roy, J. E. (2026) · The Journal of Neuroscience · doi:10.1523/jneurosci.0711-26.2026 · Cited by 4
“Analog computing uses continuous physical quantities, such as voltages or mechanical motion, to directly represent input variables and solves mathematical problems through their dynamic interactions over time (Bournez and Pouly, 2018; Hughes et al., 2019; Tzarouchis et al., 2025).”
Metadata
- Authors
- Hughes, T. W., Williamson, I. A. D., Minkov, M., & Fan, S.
- Venue
- Science Advances
- Published
- 2019-12-06
- Volume
- 5
- Issue
- 12
- Pages
- eaay6946-eaay6946
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W2996008180
- Cited by
- 364
- References
- 8
- FWCI
- 19.57
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wave physics as an analog recurrent neural network. https://app.cochat.ai/papers/10.1126/sciadv.aay6946
- The paper
- Hughes, T. W., Williamson, I. A. D., Minkov, M., & Fan, S. (2019). Wave physics as an analog recurrent neural network. Science Advances. https://doi.org/10.1126/sciadv.aay6946
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.
