Merge pull request #924 from Parallel-in-Time/bibtex-bibbot-923-356634c

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -7542,6 +7542,21 @@ @unpublished{ArrarasEtAl2025
 	year = {2025},
 }
 
+@article{BhattEtAl2025,
+	author = {Bhatt, Rishabh and Debreu, Laurent and Vidard, Arthur},
+	doi = {10.1137/24m1651903},
+	issn = {1095-7197},
+	journal = {SIAM Journal on Scientific Computing},
+	month = {April},
+	number = {2},
+	pages = {B533–B557},
+	publisher = {Society for Industrial & Applied Mathematics (SIAM)},
+	title = {Introducing Time Parallelization Within Data Assimilation},
+	url = {http://dx.doi.org/10.1137/24M1651903},
+	volume = {47},
+	year = {2025},
+}
+
 @unpublished{BossuytEtAl2025,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},