The DOI matches its Crossref record.
Lagrangian coherent structures from approximate velocity data
Abstract
This paper examines whether hyperbolic Lagrangian structures—such as stable and unstable manifolds—found in model velocity data represent reliable predictions for mixing in the true fluid velocity field. The error between the model and the true velocity field may result from velocity interpolation, extrapolation, measurement imprecisions, or any other deterministic source. We find that even large velocity errors lead to reliable predictions on Lagrangian coherent structures, as long as the errors remain small in a special time-weighted norm. More specifically, we show how model predictions from the Okubo–Weiss criterion or from finite-time Lyapunov exponents can be validated. We also estimate how close the true Lagrangian coherent structures are to those predicted by models.
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
Cited by
Closed-Loop Turbulence Control: Progress and Challenges
Brunton, S. L., & Noack, B. R. (2015) · Applied Mechanics Reviews · doi:10.1115/1.4031175 · Cited by 623
A daily global mesoscale ocean eddy dataset from satellite altimetry
Faghmous, J. H., Frenger, I., Yao, Y., Warmka, R., Lindell, A., & Kumar, V. (2015) · Scientific Data · doi:10.1038/sdata.2015.28 · Cited by 374
Spedding, G. R. (2014) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-011212-140747 · Cited by 91
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning
Krishna, K., Brunton, S. L., & Song, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3326424 · Cited by 12
“The original FTLE [9], [13], [14] primarily addressed dynamical systems of the form where the function f ( x ( t ) , t ) : R n × → n represents the system dynamics, t ∈ R represents time, and x ( t ) ∈ R n is the state of the system.”
BackgroundMethod
Metadata
- Authors
- Haller, G.
- Venue
- Physics of Fluids
- Publisher
- AIP Publishing
- Published
- 2002-06-01
- Volume
- 14
- Issue
- 6
- Pages
- 1851-1861
- ISSN
- 1070-6631 (ISSN-L)
- 1089-7666
- OpenAlex
- W1975452069
- Cited by
- 562
- References
- 0
- FWCI
- 6.21
- Type
- journal article
- Language
- en
- Open access
- closed
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: Lagrangian coherent structures from approximate velocity data. https://app.cochat.ai/papers/10.1063/1.1477449
- The paper
- Haller, G. (2002). Lagrangian coherent structures from approximate velocity data. Physics of Fluids. https://doi.org/10.1063/1.1477449
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.