The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Perspective on machine learning for advancing fluid mechanics
Open accessAbstract
A perspective is presented on how machine learning methods might advance fluid mechanics. Current limitations are discussed, though the potential impact is deemed high, so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.
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
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,870
J., J. E. (1979) · Technometrics · doi:10.1080/00401706.1979.10489803 · Cited by 3,073
Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063 · cited 1×
“The progress in machine learning that led to these advances has been mostly technological, and is well reviewed for fluid mechanics elsewhere [1].”
BackgroundPrediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865
Turbulence Modeling in the Age of Data
Duraisamy, K., Iaccarino, G., & Xiao, H. (2019) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010518-040547 · Cited by 1,508
Super-resolution reconstruction of turbulent flows with machine learning
Fukami, K., Fukagata, K., & Taira, K. (2019) · Journal of Fluid Mechanics · doi:10.1017/jfm.2019.238 · Cited by 837
Model Reduction for Flow Analysis and Control
Rowley, C. W., & Dawson, S. T. M. (2017) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010816-060042 · Cited by 792
Metadata
- Authors
- Brenner, M. P., Eldredge, J. D., & Freund, J. B.
- Venue
- Physical Review Fluids
- Publisher
- American Physical Society (APS)
- Published
- 2019-10-16
- Volume
- 4
- Issue
- 10
- ISSN
- 2469-990X (ISSN-L)
- OpenAlex
- W2981246174
- Cited by
- 292
- References
- 23
- FWCI
- 16.53
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Perspective on machine learning for advancing fluid mechanics. https://app.cochat.ai/papers/10.1103/physrevfluids.4.100501
- The paper
- Brenner, M. P., Eldredge, J. D., & Freund, J. B. (2019). Perspective on machine learning for advancing fluid mechanics. Physical Review Fluids. https://doi.org/10.1103/physrevfluids.4.100501
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.
