4 citations · 14 across the 29 of their papers we have counts for
4 papers · 1 filter
FR-Mamba: Time-Series Physical Field Reconstruction Based on State Space Model
Jiahuan Long, Wenzhe Zhang, Ning Wang +2
Physical field reconstruction (PFR) aims to predict the state distribution of physical quantities (e.g., velocity, pressure, and temperature) based on limited sensor measurements.…
: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks
Jialiang Sun, Wen Yao, Tingsong Jiang +2
The robustness of deep neural networks (DNN) models has attracted increasing attention due to the urgent need for security in many applications. Numerous existing open-sourced tool…
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
Xiaohu Zheng, Wen Yao, Zhiqiang Gong +3
For the temperature field reconstruction (TFR), a complex image-to-image regression problem, the convolutional neural network (CNN) is a powerful surrogate model due to the convolu…
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Yufeng Xia, Jun Zhang, Zhiqiang Gong +2
Deep neural networks (DNNs) have successfully learned useful data representations in various tasks. However, assessing the reliability of these representations remains a challenge.…