From 30e2085645c62496670a5333311a1aa9ec456c18 Mon Sep 17 00:00:00 2001 From: pancetta <7158893+pancetta@users.noreply.github.com> Date: Sun, 17 May 2026 13:31:25 +0000 Subject: [PATCH 1/2] updated pint.bib using bibbot --- _bibliography/pint.bib | 40 +++++++++++++++++++++++++++++++--------- 1 file changed, 31 insertions(+), 9 deletions(-) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index 531c775d..d6ac054c 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -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}, } +@misc{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author = {Hon, Sean Y. and Fung, Po Yin and Lin, Xue-lei}, doi = {10.1137/24m1702933}, @@ -8738,6 +8749,17 @@ @unpublished{KuleshovEtAl2026 year = {2026}, } +@misc{LiEtAl2026, + author = {Li, Guanglian and Lin, Qingle and Zhang, Kai and Zhou, Zhi}, + copyright = {arXiv.org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}, From 1a60bcf8bc5685059e3f6276d5b6f17037384aea Mon Sep 17 00:00:00 2001 From: Robert Speck Date: Sun, 17 May 2026 15:32:30 +0200 Subject: [PATCH 2/2] Change entries from @misc to @unpublished in bibliography --- _bibliography/pint.bib | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index d6ac054c..45723c2d 100644 --- a/_bibliography/pint.bib +++ b/_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}, @@ -8672,7 +8672,7 @@ @article{HeEtAl2026 year = {2026}, } -@misc{HessEtAl2026, +@unpublished{HessEtAl2026, author = {Hess, Florian and Götz, Florian and Durstewitz, Daniel}, copyright = {Creative Commons Attribution 4.0 International}, doi = {10.48550/ARXIV.2605.12683}, @@ -8749,7 +8749,7 @@ @unpublished{KuleshovEtAl2026 year = {2026}, } -@misc{LiEtAl2026, +@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},