Skip to content

pint.bib updates #920

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Apr 25, 2025
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 14 additions & 0 deletions _bibliography/pint.bib
Original file line number Diff line number Diff line change
Expand Up @@ -7721,6 +7721,20 @@ @article{StumpEtAl2025
year = {2025},
}

@article{TangEtAl2025,
author = {Tang, Changyang and Wu, Shu-Lin and Zhou, Tao and Zhou, Yuancheng},
doi = {10.1007/s10915-025-02899-w},
issn = {1573-7691},
journal = {Journal of Scientific Computing},
month = {April},
number = {3},
publisher = {Springer Science and Business Media LLC},
title = {Parallel-in-Time Preconditioner for the Time Spectral Methods},
url = {http://dx.doi.org/10.1007/s10915-025-02899-w},
volume = {103},
year = {2025},
}

@unpublished{WangEtAl2025,
abstract = {This paper analyzes the SParareal algorithm for stochastic differential equations (SDEs). Compared to the classical Parareal algorithm, the SParareal algorithm accelerates convergence by introducing stochastic perturbations, achieving linear convergence over unbounded time intervals. We first revisit the classical Parareal algorithm and stochastic Parareal algorithm. Then we investigate mean-square stability of the SParareal algorithm based on the stochastic $\theta$-method for SDEs, deriving linear error bounds under four sampling rules. Numerical experiments demonstrate the superiority of the SParareal algorithm in solving both linear and nonlinear SDEs, reducing the number of iterations required compared to the classical Parareal algorithm.},
author = {Huanxin Wang and Junhan Lyu and Zicheng Peng and Min Li},
Expand Down