96 citations · 502 across the 28 of their papers we have counts for
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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…
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
Zhiqiang Gong, Ping Zhong, Jiahao Qi +1
Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic…
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
Xu Liu, Wen Yao, Wei Peng +1
Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial diffe…
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…
Hybrid Finite Difference with the Physics-informed Neural Network for solving PDE in complex geometries
Zixue Xiang, Wei Peng, Weien Zhou +1
The physics-informed neural network (PINN) is effective in solving the partial differential equation (PDE) by capturing the physics constraints as a part of the training loss funct…
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
Xu Liu, Wei Peng, Zhiqiang Gong +2
Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have b…