81 citations · 146 across the 14 of their papers we have counts for
8 papers · 1 filter
RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
Zixue Xiang, Wei Peng, Wen Yao
Physics-informed neural networks (PINNs) have lately received significant attention as a representative deep learning-based technique for solving partial differential equations (PD…
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…
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
Jialiang Sun, Xiaohu Zheng, Wen Yao +3
In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system…
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…