Demo Data from EDBT 2026 / reviewers in the wild / expert
Yuhang Fu
dblp:322/6277
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2023
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 67% Performance modeling and evaluation · 33% | |
| 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 |
|---|---|---|---|---|
Performance modeling and evaluation › parallel performance evaluation
multicore scalability |
0.7 | 1 | 2023 | Toward Exascale Computation for Turbomachinery Flows · SC 2023 |
High-performance computing
performance optimization at scale |
0.7 | 1 | 2023 | Toward Exascale Computation for Turbomachinery Flows · SC 2023 |
High-performance computing
scientific computing systems |
0.7 | 1 | 2023 | Toward Exascale Computation for Turbomachinery Flows · SC 2023 |
Computational science and engineering › computational fluid dynamics
large eddy simulation |
0.2 | 1 | 2023 | Toward Exascale Computation for Turbomachinery Flows · SC 2023 |
Methods — techniques the papers use, named apart from their topics
high-order unstructured solver · 1.3heterogeneous parallel porting · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Toward Exascale Computation for Turbomachinery FlowsabstractA state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA [1], which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components. Yuhang Fu, Weiqi Shen, Jiahuan Cui, Yao Zheng 0003, Guangwen Yang 0002, Jifa Zhang, Tingwei Ji, Fangfang Xie, Xiaojing Lv, Guocheng Tao, Paul Tucker, Steven A. E. Miller, Shirui Luo, Seid Koric |
SC | 1 |
| 2022 | Controller Design for Affine Nonlinear System Based on Constructing Optimization T-S Fuzzy Model and Disturbance ReconstructionabstractAffine nonlinear systems are special kinds of nonlinear systems, and they can represent many practical systems. Dealing with affine nonlinear system based on T–S fuzzy model has drawn much attention in the literature recently. In this article, a robust control method is proposed for affine nonlinear system with disturbance based on T–S fuzzy model. First, an optimization T–S fuzzy model is constructed focusing on optimizing the parameters of the weight functions by using differential evolution algorithm. Second, in order to reconstruct the disturbance, an interval observer is designed for the affine nonlinear system, and then a novelty disturbance reconstruction method based on the interval observer is developed. The disturbance reconstruction value can asymptotically approach the actual disturbance value, and furthermore, it decouples the control input. Third, by taking the optimization T–S fuzzy model as design model and by introducing the reconstruction of disturbance into controller, a feedback robust controller with disturbance compensation is designed to stabilize the affine nonlinear system. Finally, a simulation example of the crane model is given and some comparisons are provided to verify the effectiveness and show the advantages of the proposed method. Yuhang Fu, Fanglai Zhu |
IEEE Trans. Fuzzy Syst. | 1 |