Data-driven prediction of fluid flow and temperature distribution in mar...
Recovering a globally accurate complex physics field from limited sensor...
Perception of the full state is an essential technology to support the
m...
Temperature field prediction is of great importance in the thermal desig...
Deep Neural Networks have been successfully applied in hyperspectral ima...
Transferable adversarial attack is always in the spotlight since deep
le...
As a powerful way of realizing semi-supervised segmentation, the cross
s...
Few-shot segmentation enables the model to recognize unseen classes with...
Temperature field reconstruction is essential for analyzing satellite he...
For the temperature field reconstruction (TFR), a complex image-to-image...
Physical field reconstruction is highly desirable for the measurement an...
Temperature field inversion of heat-source systems (TFI-HSS) with limite...
Recently, surrogate models based on deep learning have attracted much
at...
Physical adversarial attacks in object detection have attracted increasi...
Temperature field reconstruction of heat source systems (TFR-HSS) with
l...
Deep neural networks (DNNs) have successfully learned useful data
repres...
Temperature monitoring during the life time of heat source components in...
Thermal issue is of great importance during layout design of heat source...
Nowadays, deep learning methods, especially the convolutional neural net...
Deep learning methods have played a more and more important role in
hype...
In this paper, a novel statistical metric learning is developed for
spec...
This work develops a novel end-to-end deep unsupervised learning method ...
Machine learning methods have achieved good performance and been widely
...