The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computer Graphics Forum
Open accessAbstract
Capturing fine details of turbulence on a coarse grid is one of the main tasks in real-time fluid simulation. Existing methods for doing this have various limitations. In this paper, we propose a new turbulence method that uses a refined second vorticity confinement method, referred to as robust second vorticity confinement, and a synthesis scheme to create highly turbulent effects from coarse grid. The new technique is sufficiently stable to efficiently produce highly turbulent flows, while allowing intuitive control of vortical structures. Second vorticity confinement captures and defines the vortical features of turbulence on a coarse grid. However, due to the stability problem, it cannot be used to produce highly turbulent flows. In this work, we propose a robust formulation to improve the stability problem by making the positive diffusion term to vary with helicity adaptively. In addition, we also employ our new method to procedurally synthesize the high-resolution flow fields. As shown in our results, this approach produces stable high-resolution turbulence very efficiently.
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
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017) · arXiv · doi:10.48550/arxiv.1701.06538 · Cited by 933
Cited by
Morpho and Rvcg – Shape Analysis in R
Schlager, S. (2017) · Statistical Shape and Deformation Analysis · doi:10.1016/b978-0-12-810493-4.00011-0 · Cited by 894
MeshCNN: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322959 · Cited by 742
Amenta, N., Choi, S., & Kolluri, R. K. (2001) · Proceedings of the sixth ACM symposium on Solid modeling and applications · doi:10.1145/376957.376986 · Cited by 721
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594087 · Cited by 204
Metadata
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2021-06-17
- ISSN
- 0167-7055 (ISSN-L)
- 1467-8659
- OpenAlex
- W4241614188
- Cited by
- 1,230
- References
- 1
- Type
- paratext
- 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: Computer Graphics Forum. https://app.cochat.ai/papers/10.1111/(issn)1467-8659
- The paper
- Computer Graphics Forum. (2021). Computer Graphics Forum. https://doi.org/10.1111/(issn)1467-8659
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.