Zobrazeno 1 - 10
of 404
pro vyhledávání: '"Wang, Yanzhou"'
We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement th
Externí odkaz:
http://arxiv.org/abs/2411.07890
Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic mechanisms
Externí odkaz:
http://arxiv.org/abs/2408.12390
Autor:
Wang, Yanzhou, Al-Zogbi, Lidia, Liu, Jiawei, Shepard, Lauren, Ghazi, Ahmed, Tokuda, Junichi, Leonard, Simon, Krieger, Axel, Iordachita, Iulian
Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared
Externí odkaz:
http://arxiv.org/abs/2402.03125
Autor:
Dong, Haikuan, Shi, Yongbo, Ying, Penghua, Xu, Ke, Liang, Ting, Wang, Yanzhou, Zeng, Zezhu, Wu, Xin, Zhou, Wenjiang, Xiong, Shiyun, Chen, Shunda, Fan, Zheyong
Publikováno v:
J. Appl. Phys. 135, 161101 (2024)
Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficien
Externí odkaz:
http://arxiv.org/abs/2401.16249
Autor:
Wu, Xiguang, Zhou, Wenjiang, Dong, Haikuang, Ying, Penghua, Wang, Yanzhou, Song, Bai, Fan, Zheyong, Xiong, Shiyun
Publikováno v:
Journal of Chemical Physics 161, 014103 (2024)
Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical so
Externí odkaz:
http://arxiv.org/abs/2401.11427
Autor:
Song, Keke, Zhao, Rui, Liu, Jiahui, Wang, Yanzhou, Lindgren, Eric, Wang, Yong, Chen, Shunda, Xu, Ke, Liang, Ting, Ying, Penghua, Xu, Nan, Zhao, Zhiqiang, Shi, Jiuyang, Wang, Junjie, Lyu, Shuang, Zeng, Zezhu, Liang, Shirong, Dong, Haikuan, Sun, Ligang, Chen, Yue, Zhang, Zhuhua, Guo, Wanlin, Qian, Ping, Sun, Jian, Erhart, Paul, Ala-Nissila, Tapio, Su, Yanjing, Fan, Zheyong
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified g
Externí odkaz:
http://arxiv.org/abs/2311.04732
Publikováno v:
J. Phys.: Condens. Matter, 2024, 36, 245901
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from quantum-mechan
Externí odkaz:
http://arxiv.org/abs/2310.15314
Autor:
WANG Yanzhou, TAN Wenwei
Publikováno v:
陆军军医大学学报, Vol 46, Iss 19, Pp 2165-2170 (2024)
Ovarian cysts are one of the most common gynecological diseases, and laparoscopic surgery has been considered as the main approach for treating benign ovarian tumors. Laparoendoscopic single site surgery (LESS) is favored by women due to its hidden s
Externí odkaz:
https://doaj.org/article/7dc5a8a68c6f480b90bcca05e071fedc
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of te
Externí odkaz:
http://arxiv.org/abs/2206.07605
Autor:
Fan, Zheyong, Wang, Yanzhou, Ying, Penghua, Song, Keke, Wang, Junjie, Wang, Yong, Zeng, Zezhu, Xu, Ke, Lindgren, Eric, Rahm, J. Magnus, Gabourie, Alexander J., Liu, Jiahui, Dong, Haikuan, Wu, Jianyang, Chen, Yue, Zhong, Zheng, Sun, Jian, Erhart, Paul, Su, Yanjing, Ala-Nissila, Tapio
Publikováno v:
Journal of Chemical Physics 157, 114801 (2022)
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD. We in
Externí odkaz:
http://arxiv.org/abs/2205.10046