Zobrazeno 1 - 10
of 113
pro vyhledávání: '"Zhou, Weien"'
Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's production. Although some traditional visible copyright techniques are ava
Externí odkaz:
http://arxiv.org/abs/2304.10679
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
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. This task becomes prohibitive when such data is highly corrupted due
Externí odkaz:
http://arxiv.org/abs/2210.10646
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. Recently, deep learning surrogate assisted HSLO has been proposed, w
Externí odkaz:
http://arxiv.org/abs/2205.07812
Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial differential equations (PDEs). The key characteristic is to fix the input l
Externí odkaz:
http://arxiv.org/abs/2205.06948
Learning solutions of partial differential equations (PDEs) with Physics-Informed Neural Networks (PINNs) is an attractive alternative approach to traditional solvers due to its flexibility and ease of incorporating observed data. Despite the success
Externí odkaz:
http://arxiv.org/abs/2205.01051
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 function through the Automatic Differentiation (AD). This study proposes th
Externí odkaz:
http://arxiv.org/abs/2202.07926
Publikováno v:
Engineering Applications of Artificial Intelligence(2022)
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 been proposed to solve TFI-HSS, those existing methods ignore correlati
Externí odkaz:
http://arxiv.org/abs/2201.06880
Autor:
Wang, Donghua, Jiang, Tingsong, Sun, Jialiang, Zhou, Weien, Zhang, Xiaoya, Gong, Zhiqiang, Yao, Wen, Chen, Xiaoqian
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the v
Externí odkaz:
http://arxiv.org/abs/2109.07193