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pro vyhledávání: '"Wang, Yuliang"'
Autor:
Wang, Yuliang
This paper presents an innovative approach to computational acoustic imaging of biperiodic surfaces, exploiting the capabilities of an acoustic superlens to overcome the diffraction limit. We address the challenge of imaging physical entities in comp
Externí odkaz:
http://arxiv.org/abs/2402.17543
We study the mean-field limit of the stochastic interacting particle systems via tools from information theory. The key in our method is that, after applying the data processing inequality, one only needs to handle independent copies of solutions to
Externí odkaz:
http://arxiv.org/abs/2312.00339
Autor:
Li, Peijun, Wang, Yuliang
We propose a scheme for imaging periodic surfaces using a superlens. By employing an inverse scattering model and the transformed field expansion method, we derive an approximate reconstruction formula for the surface profile, assuming small amplitud
Externí odkaz:
http://arxiv.org/abs/2305.03086
Autor:
Dong, Lihua, Wang, Fulong, Chen, Buyun, Xia, Chenliang, Zhu, Pengwei, Tong, Zhi, Wang, Huimin, Yang, Lijun, Wang, Yuliang
Plasmonic microbubbles produced by laser irradiated gold nanoparticles (GNPs) in various liquids have emerged in numerous innovative applications. The nucleation of these bubbles inherently involves rich phenomena. In this paper, we systematically in
Externí odkaz:
http://arxiv.org/abs/2304.03885
We propose a data-assisted two-stage method for solving an inverse random source problem of the Helmholtz equation. In the first stage, the regularized Kaczmarz method is employed to generate initial approximations of the mean and variance based on t
Externí odkaz:
http://arxiv.org/abs/2303.16953
Autor:
Wang, Fulong, Wang, Huimin, Zeng, Binglin, Xia, Chenliang, Dong, Lihua, Yang, Lijun, Wang, Yuliang
Laser triggered and photothermally induced vapor bubbles have emerged as promising approaches to facilitate optomechanical energy conversion for numerous relevant applications in micro/nanofluidics. Here we report the observation of a sub-megahertz s
Externí odkaz:
http://arxiv.org/abs/2303.03967
We consider the geometric ergodicity of the Stochastic Gradient Langevin Dynamics (SGLD) algorithm under nonconvexity settings. Via the technique of reflection coupling, we prove the Wasserstein contraction of SGLD when the target distribution is log
Externí odkaz:
http://arxiv.org/abs/2301.06769
Publikováno v:
Soft Matter 13 (2017) 5381--5388
Spherical-cap-shaped interfacial nanobubbles (NBs) forming on hydrophobic surfaces in aqueous solutions have extensively been studied both from a fundamental point of view and due to their relevance for various practical applications. In this study,
Externí odkaz:
http://arxiv.org/abs/2208.08181
Autor:
Jian, Xing1 (AUTHOR) jianx@ahstu.edu.cn, Wang, Yuliang2 (AUTHOR) wangyul@ahstu.edu.cn, Li, Qiang3 (AUTHOR) liqiang9503@163.com, Miao, Yongmei2 (AUTHOR) jianx@ahstu.edu.cn
Publikováno v:
Diversity (14242818). May2024, Vol. 16 Issue 5, p305. 18p.
Autor:
Li, Lei, Wang, Yuliang
We establish a sharp uniform-in-time error estimate for the Stochastic Gradient Langevin Dynamics (SGLD), which is a popular sampling algorithm. Under mild assumptions, we obtain a uniform-in-time $O(\eta^2)$ bound for the KL-divergence between the S
Externí odkaz:
http://arxiv.org/abs/2207.09304