The DOI matches its Crossref record.
A level-set method for shape optimization
Open accessAbstract
We study a level-set method for numerical shape optimization of elastic structures. Our approach combines the level-set algorithm of Osher and Sethian with the classical shape gradient. Although this method is not specifically designed for topology optimization, it can easily handle topology changes for a very large class of objective functions. Its cost is moderate since the shape is captured on a fixed Eulerian mesh.
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
Topology optimization approaches: A comparative review
Sigmund, O., & Maute, K. (2013) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-013-0978-6 · Cited by 2,890
Structural optimization using sensitivity analysis and a level-set method
Allaire, G., Jouve, F., & Toader, A.-M. (2004) · Journal of Computational Physics · doi:10.1016/j.jcp.2003.09.032 · Cited by 2,759
Self-citationLength scale and manufacturability in density-based topology optimization
Lazarov, B. S., Wang, F., & Sigmund, O. (2016) · Archive of Applied Mechanics · doi:10.1007/s00419-015-1106-4 · Cited by 319
DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design
Kazi, R. H., Grossman, T., Cheong, H., Hashemi, A., & Fitzmaurice, G. (2017) · Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3126594.3126662 · Cited by 142
Gmeiner, F., Yang, H., Yao, L., Holstein, K., & Martelaro, N. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580999 · Cited by 130
Metadata
- Authors
- Allaire, G., Jouve, F., & Toader, A.-M.
- Venue
- Comptes Rendus. Mathématique
- Publisher
- MathDoc/Centre Mersenne
- Published
- 2002-01-01
- Volume
- 334
- Issue
- 12
- Pages
- 1125-1130
- ISSN
- 1631-073X (ISSN-L)
- 1778-3569
- OpenAlex
- W2088880127
- Cited by
- 650
- References
- 0
- FWCI
- 52.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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: A level-set method for shape optimization. https://app.cochat.ai/papers/10.1016/s1631-073x(02)02412-3
- The paper
- Allaire, G., Jouve, F., & Toader, A.-M. (2002). A level-set method for shape optimization. Comptes Rendus. Mathématique. https://doi.org/10.1016/s1631-073x(02)02412-3
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.
