8 citations · 12 across the 6 of their papers we have counts for
6 papers
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…
Multi-Level Knowledge Distillation for Speech Emotion Recognition in Noisy Conditions
Yang Liu, Haoqin Sun, Geng Chen +4
Speech emotion recognition (SER) performance deteriorates significantly in the presence of noise, making it challenging to achieve competitive performance in noisy conditions. To t…
TLNets: Transformation Learning Networks for long-range time-series prediction
Wei Wang, Yang Liu, Hao Sun
Time series prediction is a prevalent issue across various disciplines, such as meteorology, traffic surveillance, investment, and energy production and consumption. Many statistic…
RSRM: Reinforcement Symbolic Regression Machine
Yilong Xu, Yang Liu, Hao Sun
In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic r…
Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
Pu Ren, Chengping Rao, Hao Sun +1
Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network…
Encoding physics to learn reaction-diffusion processes
Chengping Rao, Pu Ren, Qi Wang +3
Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs). However, due to insuffici…