Merge pull request #967 from Parallel-in-Time/bibtex-bibbot-966-4e15f7c

pint.bib updates

Author: pancetta

_bibliography/pint.bib

@@ -7787,6 +7787,21 @@ @techreport{FalgoutSchroder2025
 	year = {2025},
 }
 
+@article{FeketeEtAl2025,
+	author = {Fekete, Imre and Izsák, Ferenc and Kupás, Vendel P. and Söderlind, Gustaf},
+	doi = {10.3390/a18080484},
+	issn = {1999-4893},
+	journal = {Algorithms},
+	month = {August},
+	number = {8},
+	pages = {484},
+	publisher = {MDPI AG},
+	title = {Tolerance Proportionality and Computational Stability in Adaptive Parallel-in-Time Runge–Kutta Methods},
+	url = {http://dx.doi.org/10.3390/a18080484},
+	volume = {18},
+	year = {2025},
+}
+
 @article{FungEtAl2025,
 	author = {Fung, Po Yin and Hon, Sean Y.},
 	doi = {10.1016/j.camwa.2025.01.019},
@@ -7815,6 +7830,15 @@ @article{GanderEtAl2025
 	year = {2025},
 }
 
+@unpublished{GuEtAl2025,
+	abstract = {This paper focuses on the efficient numerical algorithms of a three-field Biot's consolidation model. The approach begins with the introduction of innovative monolithic and global-in-time iterative decoupled algorithms, which incorporate the backward differentiation formulas for time discretization. In each iteration, these algorithms involve solving a diffusion subproblem over the entire temporal domain, followed by solving a generalized Stokes subproblem over the same time interval. To accelerate the global-in-time iterative process, we present a reduced order modeling approach based on proper orthogonal decomposition, aimed at reducing the primary computational cost from the generalized Stokes subproblem. The effectiveness of this novel method is validated both theoretically and through numerical experiments.},
+	author = {Huipeng Gu and Francesco Ballarin and Mingchao Cai and Jingzhi Li},
+	howpublished = {arXiv:2508.04082v1 [math.NA]},
+	title = {POD-based reduced order modeling of global-in-time iterative decoupled algorithms for Biot's consolidation model},
+	url = {http://arxiv.org/abs/2508.04082v1},
+	year = {2025},
+}
+
 @unpublished{HahnEtAl2025,
 	abstract = {While generally considered computationally expensive, Uncertainty Quantification using Monte Carlo sampling remains beneficial for applications with uncertainties of high dimension. As an extension of the naive Monte Carlo method, the Multi-Level Monte Carlo method reduces the overall computational effort, but is unable to reduce the time to solution in a sufficiently parallel computing environment. In this work, we propose a Uncertainty Quantification method combining Multi-Level Monte Carlo sampling and Parallel-in-Time integration for select samples, exploiting remaining parallel computing capacity to accelerate the computation. While effective at reducing the time-to-solution, Parallel-in-Time integration methods greatly increase the total computational effort. We investigate the tradeoff between time-to-solution and total computational effort of the combined method, starting from theoretical considerations and comparing our findings to two numerical examples. There, a speedup of 12 - 45% compared to Multi-Level Monte Carlo sampling is observed, with an increase of 15 - 18% in computational effort.},
 	author = {Robert Hahn and Sebastian Schöps},
@@ -8172,6 +8196,18 @@ @unpublished{ZhangEtAl2025
 	year = {2025},
 }
 
+@article{ZhangEtAl2025b,
+	author = {Zhang, Ren-Hao and Li, Jun and Jiang, Yao-Lin},
+	doi = {10.1109/lcsys.2025.3595189},
+	issn = {2475-1456},
+	journal = {IEEE Control Systems Letters},
+	pages = {1–1},
+	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
+	title = {On Krylov Subspace Implementation of ParaExp for Linear Switched Systems},
+	url = {http://dx.doi.org/10.1109/LCSYS.2025.3595189},
+	year = {2025},
+}
+
 @unpublished{ZhongEtAl2025,
 	abstract = {This paper mainly studies a direct time-parallel algorithm for solving time-dependent differential equations of order 1 to 3. Different from the traditional time-stepping approach, we directly solve the all-at-once system from higher-order evolution equations by diagonalization the time discretization matrix $B$. Based on the connection between the characteristic equation and Chebyshev polynomials, we give explicit formulas for the eigenvector matrix $V$ of $B$ and its inverse $V^{-1}$ , and prove that $cond_2\left( V \right) =\mathcal{O} \left( n^3 \right)$, where $n$ is the number of time steps. A fast algorithm $B$ designed by exploring the structure of the spectral decomposition of $B$. Numerical experiments were performed to validate the acceleration performance of the fast spectral decomposition algorithm. The results show that the proposed fast algorithm achieves significant computational speedup.},
 	author = {Shun-Zhi Zhong and Yong-Liang Zhao},
@@ -8180,3 +8216,17 @@ @unpublished{ZhongEtAl2025
 	url = {http://arxiv.org/abs/2507.05743v1},
 	year = {2025},
 }
+
+@article{HeEtAl2026,
+	author = {He, Tingting and Zhai, Tianle and Huang, Xuhang and Li, Min},
+	doi = {10.1016/j.cnsns.2025.109183},
+	issn = {1007-5704},
+	journal = {Communications in Nonlinear Science and Numerical Simulation},
+	month = {January},
+	pages = {109183},
+	publisher = {Elsevier BV},
+	title = {The parareal algorithm based on a new local time-integrator for nonlinear Caputo–Hadamard fractional differential equations},
+	url = {http://dx.doi.org/10.1016/j.cnsns.2025.109183},
+	volume = {152},
+	year = {2026},
+}