Merge pull request #884 from Parallel-in-Time/bibtex-bibbot-883-0f5e91b

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -7518,6 +7518,15 @@ @article{ZhenEtAl2024b
 	year = {2024},
 }
 
+@unpublished{BossuytEtAl2025,
+	abstract = {Time-parallel methods can reduce the wall clock time required for the accurate numerical solution of differential equations by parallelizing across the time-dimension. In this paper, we present and test the convergence behavior of a multiscale, micro-macro version of a Parareal method for stochastic differential equations (SDEs). In our method, the fine propagator of the SDE is based on a high-dimensional slow-fast microscopic model; the coarse propagator is based on a model-reduced version of the latter, that captures the low-dimensional, effective dynamics at the slow time scales. We investigate how the model error of the approximate model influences the convergence of the micro-macro Parareal algorithm and we support our analysis with numerical experiments. This is an extended and corrected version of [Domain Decomposition Methods in Science and Engineering XXVII. DD 2022, vol 149 (2024), pp. 69-76, Bossuyt, I., Vandewalle, S., Samaey, G.].},
+	author = {Ignace Bossuyt and Giovanni Samaey and Stefan Vandewalle},
+	howpublished = {arXiv:2501.19210v1 [math.NA]},
+	title = {Convergence of the micro-macro Parareal Method for a Linear Scale-Separated Ornstein-Uhlenbeck SDE: extended version},
+	url = {http://arxiv.org/abs/2501.19210v1},
+	year = {2025},
+}
+
 @article{FungEtAl2025,
 	author = {Fung, Po Yin and Hon, Sean Y.},
 	doi = {10.1016/j.camwa.2025.01.019},