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pancetta merged 2 commits into from
May 17, 2026
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pint.bib updates #1084

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updated pint.bib using bibbot
pancetta May 17, 2026
1a60bcf
Change entries from @misc to @unpublished in bibliography
pancetta May 17, 2026
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44 changes: 33 additions & 11 deletions _bibliography/pint.bib
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Expand Up @@ -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@unpublished{Ariel2015,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.
Expand Down Expand Up @@ -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@unpublished{BaumannEtAl2024b,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},
Expand Down Expand Up @@ -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},
Expand Down Expand Up @@ -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},
Expand Down Expand Up @@ -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@unpublished{HessEtAl2026,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},
Expand Down Expand Up @@ -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},
Expand Down
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