Merge pull request #890 from Parallel-in-Time/bibtex-bibbot-889-a70db78

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -7536,7 +7536,7 @@ @article{CaklovicEtAl2025
 	title = {Improving Efficiency of Parallel Across the Method Spectral Deferred Corrections},
 	url = {https://doi.org/10.1137/24M1649800},
 	volume = {47},
-	year = {2025}
+	year = {2025},
 }
 
 @article{FungEtAl2025,
@@ -7590,6 +7590,18 @@ @article{ParpasEtAl2025
 	year = {2025},
 }
 
+@article{ZengEtAl2025,
+	author = {Zeng, Xianfu and Song, Haiyan},
+	doi = {10.1016/j.matcom.2025.02.007},
+	issn = {0378-4754},
+	journal = {Mathematics and Computers in Simulation},
+	month = {February},
+	publisher = {Elsevier BV},
+	title = {Effect of space discretization on the parareal algorithm for advection-diffusion equations},
+	url = {http://dx.doi.org/10.1016/j.matcom.2025.02.007},
+	year = {2025},
+}
+
 @unpublished{ZhangEtAl2025,
 	abstract = {This paper investigates the parareal algorithms for solving the stochastic Maxwell equations driven by multiplicative noise, focusing on their convergence, computational efficiency and numerical performance. The algorithms use the stochastic exponential integrator as the coarse propagator, while both the exact integrator and the stochastic exponential integrator are used as fine propagators. Theoretical analysis shows that the mean square convergence rates of the two algorithms selected above are proportional to $k/2$, depending on the iteration number of the algorithms. Numerical experiments validate these theoretical findings, demonstrating that larger iteration numbers $k$ improve convergence rates, while larger damping coefficients $\sigma$ accelerate the convergence of the algorithms. Furthermore, the algorithms maintain high accuracy and computational efficiency, highlighting their significant advantages over traditional exponential methods in long-term simulations.},
 	author = {Liying Zhang and Qi Zhang and Lihai Ji},