Zobrazeno 1 - 10
of 1 831
pro vyhledávání: '"Rabczuk Timon"'
Publikováno v:
Nanotechnology Reviews, Vol 13, Iss 1, Pp e2304318120-7 (2024)
Concrete is the most popular construction material in infrastructure projects due to its numerous natural advantages. Nevertheless, concrete constructions frequently suffer from low tensile strength and poor durability performance which are always ur
Externí odkaz:
https://doaj.org/article/f55aa0940adb4a0699b588c8d328cffa
Autor:
Jin Yabin, He Liangshu, Wen Zhihui, Mortazavi Bohayra, Guo Hongwei, Torrent Daniel, Djafari-Rouhani Bahram, Rabczuk Timon, Zhuang Xiaoying, Li Yan
Publikováno v:
Nanophotonics, Vol 11, Iss 3, Pp 439-460 (2022)
With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficienc
Externí odkaz:
https://doaj.org/article/726cd083e0424246a3f16e2794342907
Autor:
Eshaghi, Mohammad Sadegh, Anitescu, Cosmin, Thombre, Manish, Wang, Yizheng, Zhuang, Xiaoying, Rabczuk, Timon
Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase when exploring various scenarios, such as changes in initial or boundary
Externí odkaz:
http://arxiv.org/abs/2411.06587
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:
Eshaghi, Mohammad Sadegh, Bamdad, Mostafa, Anitescu, Cosmin, Wang, Yizheng, Zhuang, Xiaoying, Rabczuk, Timon
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary conti
Externí odkaz:
http://arxiv.org/abs/2408.02698
Autor:
Wang, Yizheng, Sun, Jia, Bai, Jinshuai, Anitescu, Cosmin, Eshaghi, Mohammad Sadegh, Zhuang, Xiaoying, Rabczuk, Timon, Liu, Yinghua
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
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, Volume 367, 2020, 113066
This paper presents the development of a complete CAD-compatible framework for structural shape optimization in 3D. The boundaries of the domain are described using NURBS while the interior is discretized with B\'ezier tetrahedra. The tetrahedral mes
Externí odkaz:
http://arxiv.org/abs/2312.17575