7 citations · 13 across the 6 of their papers we have counts for
6 papers
Propagation of chaos and approximation error of random batch particle system in the mean field regime
Lei Li, Yuelin Wang, Shi Jin
The random batch method [J. Comput. Phys. 400 (2020) 108877] is not only an efficient algorithm for simulation of classical -particle systems and their mean-field limit, but als…
Asymptotic uniform estimate of random batch method with replacement for the Cucker-Smale model
Shi Jin, Yuelin Wang, Yuliang Wang
The Random Batch Method (RBM) [S. Jin, L. Li and J.-G. Liu, Random Batch Methods (RBM) for interacting particle systems, J. Comput. Phys. 400 (2020) 108877] is not only an efficien…
On the mean-field limit of the Cucker-Smale model with Random Batch Method
Yuelin Wang, Yiwen Lin
In this work, we focus on the mean-field limit of the Random Batch Method (RBM) for the Cucker-Smale model. Different from the classical mean-field limit analysis, the chaos in thi…
Propagation of chaos in path spaces via information theory
Lei Li, Yuelin Wang, Yuliang Wang
Propagation of chaos for interacting particle systems has been an active research topic over decades. We propose an alternative approach to study the mean-field limit of the stocha…
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
Jialin Chen, Yuelin Wang, Cristian Bodnar +3
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistin…
ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
Yuelin Wang, Kai Yi, Xinliang Liu +2
Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next. We mode…