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pint.bib updates #1157

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68 changes: 41 additions & 27 deletions _bibliography/pint.bib
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Expand Up @@ -7111,15 +7111,6 @@ @unpublished{GuEtAl2024
year = {2024},
}

@unpublished{GuEtAl2024b,
abstract = {In this work, we develop Crank-Nicolson-type iterative decoupled algorithms for a three-field formulation of Biot's consolidation model using total pressure. We begin by constructing an equivalent fully implicit coupled algorithm using the standard Crank-Nicolson method for the three-field formulation of Biot's model. Employing an iterative decoupled scheme to decompose the resulting coupled system, we derive two distinctive forms of Crank-Nicolson-type iterative decoupled algorithms based on the order of temporal computation and iteration: a time-stepping iterative decoupled algorithm and a global-in-time iterative decoupled algorithm. Notably, the proposed global-in-time algorithm supports a partially parallel-in-time feature. Capitalizing on the convergence properties of the iterative decoupled scheme, both algorithms exhibit second-order time accuracy and unconditional stability. Through numerical experiments, we validate theoretical predictions and demonstrate the effectiveness and efficiency of these novel approaches.},
author = {Huipeng Gu and Mingchao Cai and Jingzhi Li},
howpublished = {arXiv:2409.18391v1 [math.NA]},
title = {Crank-Nicolson-type iterative decoupled algorithms for Biot's consolidation model using total pressure},
url = {http://arxiv.org/abs/2409.18391v1},
year = {2024},
}

@article{HeEtAl2024,
author = {He, Tingting and Lu, Jian and Li, Min},
doi = {10.3934/cac.2024021},
Expand Down Expand Up @@ -7212,15 +7203,6 @@ @inproceedings{IqbalEtAl2024
year = {2024},
}

@unpublished{JackamanEtAl2024,
abstract = {Space-time finite-element discretizations are well-developed in many areas of science and engineering, but much work remains within the development of specialized solvers for the resulting linear and nonlinear systems. In this work, we consider the all-at-once solution of the discretized Navier-Stokes equations over a space-time domain using waveform relaxation multigrid methods. In particular, we show how to extend the efficient spatial multigrid relaxation methods from [37] to a waveform relaxation method, and demonstrate the efficiency of the resulting monolithic Newton-Krylov-multigrid solver. Numerical results demonstrate the scalability of the solver for varying discretization order and physical parameters.},
author = {James Jackaman and Scott MacLachlan},
howpublished = {arXiv:2407.13997v1 [math.NA]},
title = {Space-time waveform relaxation multigrid for Navier-Stokes},
url = {http://arxiv.org/abs/2407.13997v1},
year = {2024},
}

@article{JanssensEtAl2024,
author = {Janssens, N. and Meyers, J.},
doi = {10.1016/j.cpc.2023.109019},
Expand Down Expand Up @@ -8114,15 +8096,6 @@ @article{LinEtAl2025
year = {2025},
}

@unpublished{LjósheimEtAl2025,
abstract = {We study the numerical approximation of a time-dependent variational mean field game system with local couplings and either periodic or Neumann boundary conditions. Following a variational approach, we employ a finite difference discretization and solve the resulting finite-dimensional optimization problem using the Chambolle--Pock primal--dual algorithm. As this involves computing proximal operators and solving ill-conditioned linear systems at each iteration, we propose a general class of parallel-in-time preconditioners based on diagonalization techniques using discrete Fourier transforms. These enable efficient, scalable iterative solvers with robustness across a wide range of viscosities. We further develop fast solvers for the resulting ill-conditioned systems arising at each time step, using exact recursive schemes for structured grids while allowing for other geometries. Numerical experiments confirm the improved performance and parallel scalability of our approach.},
author = {Heidi Wolles Ljósheim and Dante Kalise and John W. Pearson and Francisco J. Silva},
howpublished = {arXiv:2507.01958v1 [math.NA]},
title = {Parallel-in-Time Preconditioning for Time-Dependent Variational Mean Field Games},
url = {http://arxiv.org/abs/2507.01958v1},
year = {2025},
}

@article{PamelaEtAl2025,
author = {Pamela, S.J.P. and Carey, N. and Brandstetter, J. and Akers, R. and Zanisi, L. and Buchanan, J. and Gopakumar, V. and Hoelzl, M. and Huijsmans, G. and Pentland, K. and James, T. and Antonucci, G.},
doi = {10.1016/j.cpc.2024.109391},
Expand Down Expand Up @@ -8709,6 +8682,20 @@ @article{GuEtAl2026
year = {2026},
}

@article{GuEtAl2026b,
author = {Gu, Huipeng and Cai, Mingchao and Li, Jingzhi},
doi = {10.1007/s10444-026-10361-0},
issn = {1572-9044},
journal = {Advances in Computational Mathematics},
month = {Sept },
number = {5},
publisher = {Springer Science and Business Media LLC},
title = {Crank-Nicolson-type iterative decoupled algorithms for Biot’s consolidation model using total pressure},
url = {http://dx.doi.org/10.1007/s10444-026-10361-0},
volume = {52},
year = {2026},
}

@article{HahnEtAl2026,
author = {Hahn, Robert and Schöps, Sebastian},
doi = {10.1109/tmag.2026.3651851},
Expand Down Expand Up @@ -8770,6 +8757,19 @@ @unpublished{HuangEtAl2026
year = {2026},
}

@article{JackamanEtAl2026,
author = {Jackaman, James and MacLachlan, Scott},
doi = {10.1137/24m167754x},
issn = {1095-7197},
journal = {SIAM Journal on Scientific Computing},
month = {Sept},
pages = {S555–S585},
publisher = {Society for Industrial & Applied Mathematics (SIAM)},
title = {Space-Time Waveform Relaxation Multigrid for Navier–Stokes},
url = {http://dx.doi.org/10.1137/24m167754x},
year = {2026},
}

@unpublished{JiangEtAl2026,
abstract = {We present a new training methodology for transformers using a multilevel, layer-parallel approach. Through a neural ODE formulation of transformers, our application of a multilevel parallel-in-time algorithm for the forward and backpropagation phases of training achieves parallel acceleration over the layer dimension. This dramatically enhances parallel scalability as the network depth increases, which is particularly useful for increasingly large foundational models. However, achieving this introduces errors that cause systematic bias in the gradients, which in turn reduces convergence when closer to the minima. We develop an algorithm to detect this critical transition and either switch to serial training or systematically increase the accuracy of layer-parallel training. Results, including BERT, GPT2, ViT, and machine translation architectures, demonstrate parallel-acceleration as well as accuracy commensurate with serial pre-training while fine-tuning is unaffected.},
author = {Shuai Jiang and Marc Salvado and Eric C. Cyr and Alena Kopaničáková and Rolf Krause and Jacob B. Schroder},
Expand Down Expand Up @@ -8908,6 +8908,20 @@ @article{Liu2026
year = {2026},
}

@article{LjósheimEtAl2026,
author = {Ljósheim, Heidi Wolles and Kalise, Dante and Pearson, John W. and Silva, Francisco J.},
doi = {10.1007/s10915-026-03501-7},
issn = {1573-7691},
journal = {Journal of Scientific Computing},
month = {Oct },
number = {3},
publisher = {Springer Science and Business Media LLC},
title = {Parallel-in-Time Preconditioning for Time-Dependent Variational Mean Field Games},
url = {http://dx.doi.org/10.1007/s10915-026-03501-7},
volume = {109},
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