Demo Data from SIGIR 2026 / reviewers in the wild / expert
Petr Krysl
dblp:89/6111
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2002
0000-0003-1761-5047ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
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 |
Computer animation and physical simulation · 50% Geometric modeling and processing · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › mesh processing › mesh adaptation › mesh refinement
adaptive mesh refinement |
0.0 | 1 | 2002 | CHARMS: a simple framework for adaptive simulation · ACM Trans. Graph. 2002 |
Methods — techniques the papers use, named apart from their topics
hierarchical basis refinement · 0.0finite element method · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | CHARMS: a simple framework for adaptive simulationabstractFinite element solvers are a basic component of simulation applications; they are common in computer graphics, engineering, and medical simulations. Although adaptive solvers can be of great value in reducing the often high computational cost of simulations they are not employed broadly. Indeed, building adaptive solvers can be a daunting task especially for 3D finite elements. In this paper we are introducing a new approach to produce conforming, hierarchical, adaptive refinement methods (CHARMS). The basic principle of our approach is to refine basis functions, not elements. This removes a number of implementation headaches associated with other approaches and is a general technique independent of domain dimension (here 2D and 3D), element type (e.g., triangle, quad, tetrahedron, hexahedron), and basis function order (piece-wise linear, higher order B-splines, Loop subdivision, etc.). The (un-)refinement algorithms are simple and require little in terms of data structure support. We demonstrate the versatility of our new approach through 2D and 3D examples, including medical applications and thin-shell animations. Eitan Grinspun, Petr Krysl, Peter Schröder |
ACM Trans. Graph. | 2 |