Demo Data from EDBT 2026 / reviewers in the wild / expert
Szeyu Chan
dblp:232/9392
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 2Graphics, 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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer animation and physical simulation
fluid simulation |
0.4 | 1 | 2020 | An adaptive staggered-tilted grid for incompressible flow simulation · ACM Trans. Graph. 2020 |
Computer animation and physical simulation › fluid simulation
incompressible fluid simulation |
0.4 | 1 | 2020 | An adaptive staggered-tilted grid for incompressible flow simulation · ACM Trans. Graph. 2020 |
Methods — techniques the papers use, named apart from their topics
staggered-tilted grid · 0.4octree · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Evidence for a Community of Knowledge Across Culture
Mae Fullerton, Steven A. Sloman, Szeyu Chan |
CogSci | 3 |
| 2020 | What Gives a Diagnostic Label Value? Common Use Over Informativeness
Babak Hemmatian, Szeyu Chan, Steven A. Sloman |
CogSci | 2 |
| 2020 | An adaptive staggered-tilted grid for incompressible flow simulationabstractEnabling adaptivity on a uniform Cartesian grid is challenging due to its highly structured grid cells and axis-aligned grid lines. In this paper, we propose a new grid structure - the adaptive staggered-tilted (AST) grid - to conduct adaptive fluid simulations on a regular discretization. The key mechanics underpinning our new grid structure is to allow the emergence of a new set of tilted grid cells from the nodal positions on a background uniform grid. The original axis-aligned cells, in conjunction with the populated axis-tilted cells, jointly function as the geometric primitives to enable adaptivity on a regular spatial discretization. By controlling the states of the tilted cells both temporally and spatially, we can dynamically evolve the adaptive discretizations on an Eulerian domain. Our grid structure preserves almost all the computational merits of a uniform Cartesian grid, including the cache-coherent data layout, the easiness for parallelization, and the existence of high-performance numerical solvers. Further, our grid structure can be integrated into other adaptive grid structures, such as an Octree or a sparsely populated grid, to accommodate the T-junction-free hierarchy. We demonstrate the efficacy of our AST grid by showing examples of large-scale incompressible flow simulation in domains with irregular boundaries. Yuwei Xiao, Szeyu Chan, Siqi Wang 0003, Bo Zhu 0002, Xubo Yang |
ACM Trans. Graph. | 2 |