Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Zhang, Yunyang"'
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering. General reconstruction methods for recovering the field, especially the deep learning with more paramete
Externí odkaz:
http://arxiv.org/abs/2302.11940
Perception of the full state is an essential technology to support the monitoring, analysis, and design of physical systems, one of whose challenges is to recover global field from sparse observations. Well-known for brilliant approximation ability,
Externí odkaz:
http://arxiv.org/abs/2302.09808
Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, large amounts of labeled data are required to guarantee a good prediction
Externí odkaz:
http://arxiv.org/abs/2301.06674
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 an accurate surrogate model for some complex non-linear stochastic sy
Externí odkaz:
http://arxiv.org/abs/2203.15655
As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled images. However, the wrong pseudo labeling information generated by cross
Externí odkaz:
http://arxiv.org/abs/2203.05118
Few-shot segmentation enables the model to recognize unseen classes with few annotated examples. Most existing methods adopt prototype learning architecture, where support prototype vectors are expanded and concatenated with query features to perform
Externí odkaz:
http://arxiv.org/abs/2203.04095
Temperature field reconstruction is essential for analyzing satellite heat reliability. As a representative machine learning model, the deep convolutional neural network (DCNN) is a powerful tool for reconstructing the satellite temperature field. Ho
Externí odkaz:
http://arxiv.org/abs/2202.06860
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 convolutional layer's good image feature extraction ability. However, a lot o
Externí odkaz:
http://arxiv.org/abs/2202.06596
Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive c
Externí odkaz:
http://arxiv.org/abs/2109.12482
Publikováno v:
In International Journal of Thermal Sciences September 2024 203