Merge pull request #1084 from Parallel-in-Time/bibtex-bibbot-1083-911b114

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -2436,7 +2436,7 @@ @article{Xu2014
 	year = {2014},
 }
 
-@misc{Ariel2015,
+@unpublished{Ariel2015,
 	abstract = {{We introduce a new parallel in time (parareal) algorithm which couples multiscale integrators with
 fully resolved fine scale integration and computes highly oscillatory solutions for a class of ordinary
 differential equations in parallel.
@@ -6847,7 +6847,7 @@ @article{BaumannEtAl2024
 	year = {2024},
 }
 
-@misc{BaumannEtAl2024b,
+@unpublished{BaumannEtAl2024b,
 	archiveprefix = {arXiv},
 	author = {Baumann, Thomas and G{\"o}tschel, Sebastian and Lunet, Thibaut and Ruprecht, Daniel and Speck, Robert},
 	eprint = {2412.00529},
@@ -7696,6 +7696,15 @@ @article{BhattEtAl2025
 	year = {2025},
 }
 
+@phdthesis{Bossuyt2025,
+	author = {Ignace Bossuyt},
+	month = {October},
+	school = {KU Leuven (Belgium)},
+	title = {Micro-macro Parareal methods for multiscale ordinary and stochastic differential equations},
+	url = {https://paroikos.be/thesis-ignace-bossuyt},
+	year = {2025},
+}
+
 @article{BossuytEtAl2025,
 	author = {Bossuyt, Ignace and Vandewalle, Stefan and Samaey, Giovanni},
 	doi = {10.1137/23m1609142},
@@ -7723,15 +7732,6 @@ @article{BOSSUYTEtAl2025
 	year = {2025},
 }
 
-@phdthesis{Bossuyt2025,
-	title = {Micro-macro Parareal methods for multiscale ordinary and stochastic differential equations},
-	school = {KU Leuven (Belgium)},
-	author = {Ignace Bossuyt},
-	year = {2025},
-	month = {October},
-	url = {https://paroikos.be/thesis-ignace-bossuyt},
-}
-
 @phdthesis{Bronasco2025,
 	author = {{Bronasco, Ausra}},
 	doi = {10.13097/ARCHIVE-OUVERTE/UNIGE:187048},
@@ -8672,6 +8672,17 @@ @article{HeEtAl2026
 	year = {2026},
 }
 
+@unpublished{HessEtAl2026,
+	author = {Hess, Florian and Götz, Florian and Durstewitz, Daniel},
+	copyright = {Creative Commons Attribution 4.0 International},
+	doi = {10.48550/ARXIV.2605.12683},
+	keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Distributed, Parallel, and Cluster Computing (cs.DC), Computational Physics (physics.comp-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
+	publisher = {arXiv},
+	title = {Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction},
+	url = {https://arxiv.org/abs/2605.12683},
+	year = {2026},
+}
+
 @article{HonEtAl2026,
 	author = {Hon, Sean Y. and Fung, Po Yin and Lin, Xue-lei},
 	doi = {10.1137/24m1702933},
@@ -8738,6 +8749,17 @@ @unpublished{KuleshovEtAl2026
 	year = {2026},
 }
 
+@unpublished{LiEtAl2026,
+	author = {Li, Guanglian and Lin, Qingle and Zhang, Kai and Zhou, Zhi},
+	copyright = {arXiv.org perpetual, non-exclusive license},
+	doi = {10.48550/ARXIV.2605.11979},
+	keywords = {Numerical Analysis (math.NA), FOS: Mathematics, FOS: Mathematics, 65M55},
+	publisher = {arXiv},
+	title = {Optimized Two-Step Coarse Propagators in Parareal Algorithms},
+	url = {https://arxiv.org/abs/2605.11979},
+	year = {2026},
+}
+
 @unpublished{LuEtAl2026,
 	abstract = {Parabolic optimal control problems arise in numerous scientific and engineering applications. They typically lead to large-scale coupled forward-backward systems that cannot be treated with classical time-stepping schemes and are computationally expensive to solve. Therefore, parallel methods are essential to reduce the computational time required. In this work, we investigate a time domain decomposition approach, namely the time parallel Schwarz method, applied to parabolic optimal control problems. We analyze the convergence behavior and focus on the weak scalability property of this method as the number of time intervals increases. To characterize the spectral radius of the iteration matrix, we present two analysis techniques: the construction of a tailored matrix norm and the application of block Toeplitz matrix theory. Our analyses yield both nonasymptotic bounds on the spectral radius and an asymptotic characterization of the eigenvalues as the number of time intervals tends to infinity. Numerical experiments further confirm our theoretical findings and demonstrate the weak scalability of the time parallel Schwarz method. This work introduces the first theoretical tool for analyzing the weak scalability of time domain decomposition methods, and our results shed light on the suitability of our algorithm for large-scale simulations on modern high-performance computing architectures.},
 	author = {Liu-Di Lu and Tommaso Vanzan},