1 citations · 1 across the 2 of their papers we have counts for
7 papers
Exponential Low-Regularity Parareal Algorithms for Nonlinear Schrödinger Equations
Qingle Lin, Zhi Zhou
The parareal algorithm is one of the most widely studied parallel-in-time methods for the numerical approximation of time-dependent problems. For non-diffusive equations, however,…
Linear Convergence of Parareal Algorithm for Semilinear Parabolic Equations
Guanglian Li, Qingle Lin, Shu-lin Wu +1
Long-time simulations of evolution equations present substantial computational challenges due to the inherently sequential nature of conventional time-stepping schemes. The pararea…
Dual Variational Neural Network for the -Laplace Problem
Tianhao Hu, Guanglian Li, Fengru Wang +2
The reliable and accurate numerical approximation of the -Laplacian is particularly challenging in the extreme regimes and , where the operator becomes ei…
Convergence analysis of a parareal algorithm with multistep fine propagator
Georgios Akrivis, Qingle Lin, Zhi Zhou
The parareal algorithm is a powerful parallel-in-time integration method that accelerates the numerical solution of evolution equations by iteratively combining a fine propagator a…
Optimized Two-Step Coarse Propagators in Parareal Algorithms
Guanglian Li, Qingle Lin, Kai Zhang +1
In this work, we propose a novel framework for accelerating the parareal algorithm, in which the coarse propagator is formulated as a two-step method and optimized with respect to…
Regularity Analysis and High-Order Time Stepping Scheme for Quasilinear Subdiffusion
Bangti Jin, Qimeng Quan, Barbara Wohlmuth +1
In this work, we investigate a quasilinear subdiffusion model which involves a fractional derivative of order in time and a nonlinear diffusion coefficient. First, usi…