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Szeyu Chan

dblp:232/9392 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer animation and physical simulation
fluid simulation
0.412020
An adaptive staggered-tilted grid for incompressible flow simulation · ACM Trans. Graph. 2020
Computer animation and physical simulation › fluid simulation
incompressible fluid simulation
0.412020
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
YearPublicationVenuePosition
2020 Evidence for a Community of Knowledge Across Culture
Mae Fullerton, Steven A. Sloman, Szeyu Chan
CogSci3
2020 What Gives a Diagnostic Label Value? Common Use Over Informativeness
Babak Hemmatian, Szeyu Chan, Steven A. Sloman
CogSci2
2020 An adaptive staggered-tilted grid for incompressible flow simulation
abstract
Enabling 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