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04/14/2023
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
Data-driven prediction of fluid flow and temperature distribution in mar...
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02/23/2023
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor...
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02/20/2023
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
Perception of the full state is an essential technology to support the m...
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01/17/2023
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Temperature field prediction is of great importance in the thermal desig...
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05/25/2022
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
Deep Neural Networks have been successfully applied in hyperspectral ima...
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05/19/2022
Transferable Physical Attack against Object Detection with Separable Attention
Transferable adversarial attack is always in the spotlight since deep le...
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03/10/2022
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
As a powerful way of realizing semi-supervised segmentation, the cross s...
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03/08/2022
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
Few-shot segmentation enables the model to recognize unseen classes with...
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02/14/2022
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
Temperature field reconstruction is essential for analyzing satellite he...
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02/14/2022
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
For the temperature field reconstruction (TFR), a complex image-to-image...
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01/26/2022
A deep learning method based on patchwise training for reconstructing temperature field
Physical field reconstruction is highly desirable for the measurement an...
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01/18/2022
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
Temperature field inversion of heat-source systems (TFI-HSS) with limite...
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09/26/2021
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
Recently, surrogate models based on deep learning have attracted much at...
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09/15/2021
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Physical adversarial attacks in object detection have attracted increasi...
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08/17/2021
TFRD: A Benchmark Dataset for Research on Temperature Field Reconstruction of Heat-Source Systems
Temperature field reconstruction of heat source systems (TFR-HSS) with l...
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07/15/2021
Randomized ReLU Activation for Uncertainty Estimation of Deep Neural Networks
Deep neural networks (DNNs) have successfully learned useful data repres...
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06/22/2021
Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems
Temperature monitoring during the life time of heat source components in...
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03/20/2021
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Thermal issue is of great importance during layout design of heat source...
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12/28/2019
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
Nowadays, deep learning methods, especially the convolutional neural net...
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12/24/2019
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in hype...
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05/13/2019
A novel statistical metric learning for hyperspectral image classification
In this paper, a novel statistical metric learning is developed for spec...
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03/18/2019
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
This work develops a novel end-to-end deep unsupervised learning method ...
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07/04/2018
