18 citations · 25 across the 6 of their papers we have counts for
6 papers
Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks
Wei Peng, Wen Yao, Weien Zhou +2
Physics-informed neural networks (PINNs) have been proposed to solve two main classes of problems: data-driven solutions and data-driven discovery of partial differential equations…
Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites
Zeyu Cao, Wen Yao, Wei Peng +2
The rapid analysis of thermal stress and deformation plays a pivotal role in the thermal control measures and optimization of the structural design of satellites. For achieving rea…
RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks
Wei Peng, Weien Zhou, Xiaoya Zhang +2
Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its fl…
Consistency regularization-based Deep Polynomial Chaos Neural Network Method for Reliability Analysis
Xiaohu Zheng, Wen Yao, Yunyang Zhang +1
Polynomial chaos expansion (PCE) is a powerful surrogate model-based reliability analysis method. Generally, a PCE model with a higher expansion order is usually required to obtain…
A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain
Kairui Bao, Wen Yao, Xiaoya Zhang +2
In the whole aircraft structural optimization loop, thermal analysis plays a very important role. But it faces a severe computational burden when directly applying traditional nume…
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +2
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is…