182 citations · 253 across the 6 of their papers we have counts for
6 papers
In-depth Analysis on Caching and Pre-fetching in Mixture of Experts Offloading
Shuning Lin, Yifan He, Yitong Chen
In today's landscape, Mixture of Experts (MoE) is a crucial architecture that has been used by many of the most advanced models. One of the major challenges of MoE models is that t…
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…
Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations
Shuning Lin, Yong Chen
We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coefficient discovery in order to overcome deficiency of the discrete…
Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions
Shuning Lin, Yong Chen
We put forth two physics-informed neural network (PINN) schemes based on Miura transformations and the novelty of this research is the incorporation of Miura transformation constra…
A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions
Shuning Lin, Yong Chen
With the advantages of fast calculating speed and high precision, the physics-informed neural network method opens up a new approach for numerically solving nonlinear partial diffe…