Zobrazeno 1 - 10
of 856
pro vyhledávání: '"LIU Yinghua"'
Autor:
Li LiYa, Zhang XinSheng, Huang Xiang, Liu Zhao, Liu Lu, Lv XiuMing, Li Ye, Chen Jing, Zhang KeMing, Wang HongChi, Xia Jing, Cong Yang, Cui Xiu, Long HongBo, You ShuQin, Liu Fang, Liu YingHua
Publikováno v:
Frontiers in Nutrition, Vol 11 (2024)
ObjectiveThis study aims to examine the nutritional status of individuals diagnosed with esophageal cancer and compare the nutritional indicators and intestinal flora between malnourished and non-malnourished patients. The findings aim to contribute
Externí odkaz:
https://doaj.org/article/defd2fc487db4e30ac8dc5c570b9f76a
Autor:
Bai, Jinshuai, Lin, Zhongya, Wang, Yizheng, Wen, Jiancong, Liu, Yinghua, Rabczuk, Timon, Gu, YuanTong, Feng, Xi-Qiao
Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various conditions. In this work, we propose an energy-based physics-informed neur
Externí odkaz:
http://arxiv.org/abs/2411.03671
Autor:
Wang, Yizheng, Bai, Jinshuai, Lin, Zhongya, Wang, Qimin, Anitescu, Cosmin, Sun, Jia, Eshaghi, Mohammad Sadegh, Gu, Yuantong, Feng, Xi-Qiao, Zhuang, Xiaoying, Rabczuk, Timon, Liu, Yinghua
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespre
Externí odkaz:
http://arxiv.org/abs/2410.19843
Autor:
Wang, Yizheng, Sun, Jia, Bai, Jinshuai, Anitescu, Cosmin, Eshaghi, Mohammad Sadegh, Zhuang, Xiaoying, Rabczuk, Timon, Liu, Yinghua
Publikováno v:
Comput. Methods Appl. Mech. Engrg. 433 (2025) 117518
AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of Kolmogorov-Arnold Network (KAN) indicates that there is potential to re
Externí odkaz:
http://arxiv.org/abs/2406.11045
Autor:
Wang, Yizheng, Li, Xiang, Yan, Ziming, Du, Yuqing, Bai, Jinshuai, Liu, Bokai, Rabczuk, Timon, Liu, Yinghua
Homogenization is an essential tool for studying multiscale physical phenomena. However, traditional numerical homogenization, heavily reliant on finite element analysis, requires extensive computation costs, particularly in handling complex geometri
Externí odkaz:
http://arxiv.org/abs/2404.07943
As one of the most important topics studied in creep fracture mechanics, mechanics fields at three-dimensional (3D) sharp V-notches and crack tip have drawn tremendous attentions. With many years efforts on constraint theory developed in creeping sol
Externí odkaz:
http://arxiv.org/abs/2308.02809
Publikováno v:
Int J Numer Methods Eng. 2024;e7585
In recent years, the rapid advancement of deep learning has significantly impacted various fields, particularly in solving partial differential equations (PDEs) in the realm of solid mechanics, benefiting greatly from the remarkable approximation cap
Externí odkaz:
http://arxiv.org/abs/2302.01538
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, 410 (2023) 116012
We proposed the boundary-integral type neural networks (BINN) for the boundary value problems in computational mechanics. The boundary integral equations are employed to transfer all the unknowns to the boundary, then the unknowns are approximated us
Externí odkaz:
http://arxiv.org/abs/2301.04480
Publikováno v:
In Engineering Fracture Mechanics 8 November 2024 310
Autor:
Wang, Yizheng, Sun, Jia, Bai, Jinshuai, Anitescu, Cosmin, Eshaghi, Mohammad Sadegh, Zhuang, Xiaoying, Rabczuk, Timon, Liu, Yinghua
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 January 2025 433 Part B