Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Zuo, Xinzhe"'
Sampling from a target distribution is a fundamental problem. Traditional Markov chain Monte Carlo (MCMC) algorithms, such as the unadjusted Langevin algorithm (ULA), derived from the overdamped Langevin dynamics, have been extensively studied. From
Externí odkaz:
http://arxiv.org/abs/2410.08987
We provide a numerical analysis and computation of neural network projected schemes for approximating one dimensional Wasserstein gradient flows. We approximate the Lagrangian mapping functions of gradient flows by the class of two-layer neural netwo
Externí odkaz:
http://arxiv.org/abs/2402.16821
We provide a Lyapunov convergence analysis for time-inhomogeneous variable coefficient stochastic differential equations (SDEs). Three typical examples include overdamped, irreversible drift, and underdamped Langevin dynamics. We first formula the pr
Externí odkaz:
http://arxiv.org/abs/2402.01036
In arXiv:2305.03945 [math.NA], a first-order optimization algorithm has been introduced to solve time-implicit schemes of reaction-diffusion equations. In this research, we conduct theoretical studies on this first-order algorithm equipped with a qua
Externí odkaz:
http://arxiv.org/abs/2401.14602
Autor:
Zuo, Xinzhe, Chou, Tom
Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process that enhances transcription fidelity. We model the backtracking mechanism of RNA polymerase, which usually h
Externí odkaz:
http://arxiv.org/abs/2103.05151
Autor:
Zuo, Xinzhe, Porter, Mason A
Publikováno v:
Phys. Rev. E 103, 022304 (2021)
The study of temporal networks in discrete time has yielded numerous insights into time-dependent networked systems in a wide variety of applications. For many complex systems, however, it is useful to develop continuous-time models of networks and t
Externí odkaz:
http://arxiv.org/abs/1906.09394
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