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Ondine Chanon

dblp:262/9869 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-9609-4635ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Geometric modeling and processing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › mesh processing › mesh adaptation › mesh refinement
adaptive mesh refinement
0.812024
A Shape Derivative Approach to Domain Simplification · Comput. Aided Des. 2024
Geometric modeling and processing
isogeometric analysis
0.812024
A Shape Derivative Approach to Domain Simplification · Comput. Aided Des. 2024

Methods — techniques the papers use, named apart from their topics

shape sensitivity analysis · 0.8a posteriori error estimation · 0.8
YearPublicationVenuePosition
2024 A Shape Derivative Approach to Domain Simplification
abstract
The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. This enables faster and more efficient simulations, without sacrificing accuracy in the regions of interest. More precisely, we consider an isogeometric discretisation of an elliptic model problem defined on a two-dimensional simply connected hierarchical B-spline physical domain with a complex boundary. Starting with an oversimplification of the geometry, we build a goal-oriented adaptive strategy that adaptively reintroduces continuous geometrical features in regions where the analysis suggests a large impact on the quantity of interest. This strategy is driven by an a posteriori estimator of the defeaturing error based on first-order shape sensitivity analysis, and it profits from the local refinement properties of hierarchical B-splines. The adaptive algorithm is described together with a procedure to generate (partially) simplified hierarchical B-spline geometrical domains. Numerical experiments are presented to illustrate the proposed strategy and its limitations.
Jochen Hinz, Ondine Chanon, Alessandra Arrigoni, Annalisa Buffa
Comput. Aided Des.2