activity
20222024
most citedAbstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

6 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

physics.flu-dyn2024

Numerical simulations of attachment-line boundary layer in hypersonic flow, Part II: the features of three-dimensional turbulent boundary layer

Youcheng Xi, Bowen Yan, Guangwen Yang +1

In this study,we investigate the characteristics of three-dimensional turbulent boundary layers influenced by transverse flow and pressure gradients. Our findings reveal that even…

physics.flu-dyn2024

Numerical simulations of attachment-line boundary layer in hypersonic flow, Part I: roughness-induced subcritical transitions

Youcheng Xi, Bowen Yan, Guangwen Yang +3

The attachment-line boundary layer is critical in hypersonic flows because of its significant impact on heat transfer and aerodynamic performance. In this study, high-fidelity nume…

cs.SE2023

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Weijie Shao, Yuyang Gao, Fu Song +3

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing cli…

physics.flu-dyn2023

Development of a novel nonlinear dynamic cavitation model and its numerical validations

Haidong Yu, Xiaobo Quan, Haipeng Wei +2

Aiming at modeling the cavitation bubble cluster, we propose a novel nonlinear dynamic cavitation model (NDCM) considering the second derivative term in Rayleigh-Plesset equation t…

cs.SE20226 cited

Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

Jiaxiang Liu, Yunhan Xing, Xiaomu Shi +3

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical dom…