EDBT 2026 Demo / reviewers in the wild / expert
Boshun Gao
dblp:274/0994
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering
computational fluid dynamics |
0.5 | 1 | 2021 | Scalable adaptive PDE solvers in arbitrary domains · SC 2021 |
High-performance computing › scientific computing systems
adaptive mesh refinement |
0.5 | 1 | 2021 | Scalable adaptive PDE solvers in arbitrary domains · SC 2021 |
High-performance computing › scientific computing systems
partial differential equation solver |
0.5 | 1 | 2021 | Scalable adaptive PDE solvers in arbitrary domains · SC 2021 |
High-performance computing
scientific computing systems |
0.5 | 1 | 2021 | Scalable adaptive PDE solvers in arbitrary domains · SC 2021 |
Methods — techniques the papers use, named apart from their topics
octree · 1.0finite element method · 1.0adaptive discretization · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Scalable adaptive PDE solvers in arbitrary domainsabstractEfficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of a `good' adaptively-refined mesh. In this work, we present an efficient novel octree-based adaptive discretization approach capable of carving out arbitrarily shaped void regions from the parent domain: an essential requirement for fluid simulations around complex objects. Carving out objects produces an incomplete octree. We develop efficient top-down and bottom-up traversal methods to perform finite element computations on incomplete octrees. We validate the framework by (a) showing appropriate convergence analysis and (b) computing the drag coefficient for flow past a sphere for a wide range of Reynolds numbers (O(1 - 106)) encompassing the drag crisis regime. Finally, we deploy the framework on a realistic geometry on a current project to evaluate COVID-19 transmission risk in classrooms. Masado Ishii, Milinda Fernando, Boshun Gao, Kendrick Tan, Ming-Chen Hsu, Adarsh Krishnamurthy, Hari Sundar, Baskar Ganapathysubramanian |
SC | 4 |