Zobrazeno 1 - 10
of 1 157
pro vyhledávání: '"Zhang, Jun Jie"'
Autor:
Zhang, Jun-Jie, Cheng, Nan, Li, Fu-Peng, Wang, Xiu-Cheng, Chen, Jian-Nan, Pang, Long-Gang, Meng, Deyu
Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles that govern
Externí odkaz:
http://arxiv.org/abs/2409.06402
Unmanned Aerial Vehicles (UAVs), due to their low cost and high flexibility, have been widely used in various scenarios to enhance network performance. However, the optimization of UAV trajectories in unknown areas or areas without sufficient prior i
Externí odkaz:
http://arxiv.org/abs/2409.00036
Autor:
Ma, Longfei, Cheng, Nan, Wang, Xiucheng, Chen, Jiong, Gao, Yinjun, Zhang, Dongxiao, Zhang, Jun-Jie
The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict t
Externí odkaz:
http://arxiv.org/abs/2406.13145
We present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell's equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand the interplay between charge density, current densi
Externí odkaz:
http://arxiv.org/abs/2402.16920
Autor:
Zhou, Zhou, Zhang, Jun-Jie, Turner, Gemma F., Moggach, Stephen A., Lekina, Yulia, Morris, Samuel, Wang, Shun, Hu, Yiqi, Li, Qiankun, Xue, Jinshuo, Feng, Zhijian, Yan, Qingyu, Weng, Yuyan, Xu, Bin, Fang, Yong, Shen, Ze Xiang, Fang, Liang, Dong, Shuai, You, Lu
Publikováno v:
Applied Physics Reviews 11, 011414 (2024)
Interlayer stacking order has recently emerged as a unique degree of freedom to control crystal symmetry and physical properties in two-dimensional van der Waals (vdW) materials and heterostructures. By tuning the layer stacking pattern, symmetry-bre
Externí odkaz:
http://arxiv.org/abs/2402.13639
Autor:
Zhang, Jun-Jie, Meng, Deyu
Neural networks demonstrate inherent vulnerability to small, non-random perturbations, emerging as adversarial attacks. Such attacks, born from the gradient of the loss function relative to the input, are discerned as input conjugates, revealing a sy
Externí odkaz:
http://arxiv.org/abs/2402.10983
This paper presents the RBG-Maxwell framework, a relativistic collisional plasma simulator on GPUs. We provide detailed discussions on the fundamental equations, numerical algorithms, implementation specifics, and key testing outcomes. The RBG-Maxwel
Externí odkaz:
http://arxiv.org/abs/2308.04869
Autor:
Zhang, Jun-Jie1 (AUTHOR), Chen, Jian-Nan1 (AUTHOR), Meng, De-Yu2,3 (AUTHOR) dymeng@mail.xjtu.edu.cn, Wang, Xiu-Cheng4 (AUTHOR)
Publikováno v:
Scientific Reports. 11/18/2024, Vol. 14 Issue 1, p1-10. 10p.
JefiPIC: A 3-D Full Electromagnetic Particle-in-Cell Simulator Based on Jefimenko's Equations on GPU
This paper presents a novel 3-D full electromagnetic particle-in-cell (PIC) code called JefiPIC, which uses Jefimenko's equations as the electromagnetic (EM) field solver through a full-space integration method. Leveraging the power of state-of-the-a
Externí odkaz:
http://arxiv.org/abs/2209.07944
Despite the successes in many fields, it is found that neural networks are difficult to be both accurate and robust, i.e., high accuracy networks are often vulnerable. Various empirical and analytic studies have substantiated that there is more or le
Externí odkaz:
http://arxiv.org/abs/2205.01493