211 citations · 225 across the 7 of their papers we have counts for
7 papers
Traveling waves to a logarithmic chemotaxis model with fast diffusion and singularities
Xiaowen Li, Dongfang Li, Jingyu Li +1
This paper is concerned with a chemotaxis model with logarithmic sensitivity and fast diffusion, which possesses strong singularities for the sensitivity at zero-concentration of c…
Data-driven method to learn the most probable transition pathway and stochastic differential equations
Jianyu Hu, Dongfang Li, Jinqiao Duan +1
Transition phenomena between metastable states play an important role in complex systems due to noisy fluctuations. In this paper, the physics informed neural networks (PINNs) are…
Unconditionally optimal convergence of an energy-conserving and linearly implicit scheme for nonlinear wave equations
Waixiang Cao, Dongfang Li, Zhimin Zhang
In this paper, we present and analyze an energy-conserving and linearly implicit scheme for solving the nonlinear wave equations. Optimal error estimates in time and superconvergen…
Sharp pointwise-in-time error estimate of L1 scheme for nonlinear subdiffusion equations
Dongfang Li, Hongyu Qin, Jiwei Zhang
An essential feature of the subdiffusion equations with the -order time fractional derivative is the weak singularity at the initial time. The weak regularity of the solution is…
Analysis of -Galerkin FEMs for time-fractional nonlinear parabolic problems
Dongfang Li, Hong-lin Liao, Weiwei Sun +2
This paper is concerned with numerical solutions of time-fractional nonlinear parabolic problems by a class of -Galerkin finite element methods. The analysis of methods fo…
A Novel Sufficient Condition for Generalized Orthogonal Matching Pursuit
Jinming Wen, Zhengchun Zhou, Dongfang Li +1
Generalized orthogonal matching pursuit (gOMP), also called orthogonal multi-matching pursuit, is an extension of OMP in the sense that indices are identified per iteratio…
Stable Recovery of Sparse Signals via Minimization
Jinming Wen, Dongfang Li, Fumin Zhu
In this paper, we show that, under the assumption that $\|\e\|_2\leq ε$, every sparse signal $\x\in \mathbb{R}^n$ can be stably () or exactly recovered () from $\y…