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pro vyhledávání: '"Liu, HongSheng"'
Autor:
Wang, Qi, Ren, Pu, Zhou, Hao, Liu, Xin-Yang, Deng, Zhiwen, Zhang, Yi, Chengze, Ruizhi, Liu, Hongsheng, Wang, Zidong, Wang, Jian-Xun, Ji-Rong_Wen, Sun, Hao, Liu, Yang
When solving partial differential equations (PDEs), classical numerical methods often require fine mesh grids and small time stepping to meet stability, consistency, and convergence conditions, leading to high computational cost. Recently, machine le
Externí odkaz:
http://arxiv.org/abs/2411.00040
Autor:
Zeng, Bocheng, Wang, Qi, Yan, Mengtao, Liu, Yang, Chengze, Ruizhi, Zhang, Yi, Liu, Hongsheng, Wang, Zidong, Sun, Hao
Solving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotemporal dynamics (e.g., tremendo
Externí odkaz:
http://arxiv.org/abs/2410.01337
Autor:
Ye, Zhanhong, Huang, Xiang, Chen, Leheng, Liu, Zining, Wu, Bingyang, Liu, Hongsheng, Wang, Zidong, Dong, Bin
This paper introduces PDEformer-1, a versatile neural solver capable of simultaneously addressing various partial differential equations (PDEs). With the PDE represented as a computational graph, we facilitate the seamless integration of symbolic and
Externí odkaz:
http://arxiv.org/abs/2407.06664
Autor:
Zhao, Luneng, Liu, Hongsheng, Chang, Yuan, Shi, Xiaoran, Gao, Junfeng, Zhao, Jijun, Ding, Feng
The primary restrictions on 2D transition metal dichalcogenides (TMD) vdW heterostructures (vdWHs) are size limitation and alloying. Recently, a two-step vapor deposition method was reported to grow wafer-scale TMD vdWHs with little contamination [Na
Externí odkaz:
http://arxiv.org/abs/2405.04939
Autor:
Yang, Tianzhou, Zhang, Li, Yi, Liwei, Feng, Huawei, Li, Shimeng, Chen, Haoyu, Zhu, Junfeng, Zhao, Jian, Zeng, Yingyue, Liu, Hongsheng
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 6, p e15431 (2020)
BackgroundEarly diabetes screening can effectively reduce the burden of disease. However, natural population–based screening projects require a large number of resources. With the emergence and development of machine learning, researchers have star
Externí odkaz:
https://doaj.org/article/9cbf85428907484f8caa18ac59996c4b
This paper introduces PDEformer, a neural solver for partial differential equations (PDEs) capable of simultaneously addressing various types of PDEs. We propose to represent the PDE in the form of a computational graph, facilitating the seamless int
Externí odkaz:
http://arxiv.org/abs/2402.12652
Autor:
Shi, Xiaoran, Gao, Weiwei, Liu, Hongsheng, Fu, Zhen-Guo, Zhang, Gang, Zhang, Yong-Wei, Gao, Junfeng, Zhao, Jijun
Design and synthesis of novel two-dimensional (2D) materials that possess robust structural stability and unusual physical properties may open up enormous opportunities for device and engineering applications. Herein we propose a 2D sumanene lattice
Externí odkaz:
http://arxiv.org/abs/2311.07273
Publikováno v:
Advanced Energy Materials, 2023, 13, 2301331
Bilayer borophene, very recently synthesized on Ag and Cu, possesses extremely flat large surface and excellent conductivity. Besides, the van der Waals gap of bilayer borophene can be intercalated by metal atoms, thereby tailoring the properties of
Externí odkaz:
http://arxiv.org/abs/2309.02963
Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention. However, most existing methods adopt pure data-driven black-box models, which have limited accuracy and interpretability. By combining trainable dif
Externí odkaz:
http://arxiv.org/abs/2307.14395