1 citations · 2 across the 4 of their papers we have counts for
5 papers
FAME: Adaptive Functional Attention with Expert Routing for Function-on-Function Regression
Yifei Gao, Yong Chen, Chen Zhang
Functional data play a pivotal role across science and engineering, yet their infinite-dimensional nature makes representation learning challenging. Conventional statistical models…
Gradient-enhanced PINN with residual unit for studying forward-inverse problems of variable coefficient equations
Hui-Juan Zhou, Yong Chen
Physics-informed neural network (PINN) is a powerful emerging method for studying forward-inverse problems of partial differential equations (PDEs), even from limited sample data.…
Causality-guided adaptive sampling method for physics-informed neural networks
Shuning Lin, Yong Chen
Compared to purely data-driven methods, a key feature of physics-informed neural networks (PINNs) - a proven powerful tool for solving partial differential equations (PDEs) - is th…
The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves
Shuning Lin, Yong Chen
Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the…
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Yongchao Liu, Yue Jin, Yong Chen +4
Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions t…