5 papers
McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation
Jiwei Jia, Xinliang Liu, Juntao Wang +1
Solving heterogeneous Helmholtz equations at high wavenumbers remains challenging because the discretized operator is indefinite, pollution degrades phase accuracy, and scalar coar…
Born-Preconditioned Learning for Iterative Neural PDE Solvers
Juntao Wang, Jiwei Jia, Xinliang Liu
Simulating high-frequency waves in spatially varying media remains challenging for both classical iterative methods and neural PDE solvers. We propose Neural Preconditioned Born Se…
Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
Richard Cornelius Suwandi, Feng Yin, Juntao Wang +3
The efficiency of Bayesian optimization (BO) relies heavily on the choice of the Gaussian process (GP) kernel, which plays a central role in balancing exploration and exploitation…
Learning to Gridize: Segment Physical World by Wireless Communication Channel
Juntao Wang, Feng Yin, Tian Ding +3
Gridization, the process of partitioning space into grids where users share similar channel characteristics, serves as a fundamental prerequisite for efficient large-scale network…
Attentional Graph Neural Network Is All You Need for Robust Massive Network Localization
Wenzhong Yan, Feng Yin, Juntao Wang +3
In this paper, we design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. W…