3 papers
cs.LG2025
PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows
Huaguan Chen, Yang Liu, Hao Sun
Accurately predicting fluid dynamics and evolution has been a long-standing challenge in physical sciences. Conventional deep learning methods often rely on the nonlinear modeling…
cs.LG2024
Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data
Shou Nakano, Yang Liu
This paper focuses on explaining changes over time in globally-sourced, annual temporal data, with the specific objective of identifying pivotal factors that contribute to these te…
stat.ME2023
A novel approach of empirical likelihood with massive data
Yang Liu, Xia Chen, Wei-min Yang
In this paper, we propose a novel approach for tackling the obstacles of empirical likelihood in the face of massive data, which is called split sample mean empirical likelihood (S…