From d31d7dc058b6d63af90d4caecb1fbc1a9836d22b Mon Sep 17 00:00:00 2001 From: pancetta <7158893+pancetta@users.noreply.github.com> Date: Sun, 15 Mar 2026 13:29:55 +0000 Subject: [PATCH 1/3] updated pint.bib using arxivbot --- _bibliography/pint.bib | 320 +++++++++++++++++++++++++---------------- 1 file changed, 196 insertions(+), 124 deletions(-) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index 13934b4d..4af3c9ac 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -6321,15 +6321,6 @@ @unpublished{BoschEtAl2023 year = {2023}, } -@unpublished{BossuytEtAl2023, - abstract = {We propose a micro-macro parallel-in-time Parareal method for scalar McKean-Vlasov stochastic differential equations (SDEs). In the algorithm, the fine Parareal propagator is a Monte Carlo simulation of an ensemble of particles, while an approximate ordinary differential equation (ODE) description of the mean and the variance of the particle distribution is used as a coarse Parareal propagator to achieve speedup. We analyse the convergence behaviour of our method for a linear problem and provide numerical experiments indicating the parallel weak scaling of the algorithm on a set of examples. We show that convergence typically takes place in a low number of iterations, depending on the quality of the ODE predictor. For bimodal SDEs, we avoid quality deterioration of the coarse predictor (compared to unimodal SDEs) through the usage of multiple ODEs, each describing the mean and variance of the particle distribution in locally unimodal regions of the phase space. The benefit of the proposed algorithm can be viewed through two lenses: (i) through the parallel-in-time lens, speedup is obtained through the use of a very cheap coarse integrator (an ODE moment model), and (ii) through the moment models lens, accuracy is iteratively gained through the use of parallel machinery as a corrector. In contrast to the isolated use of a moment model, the proposed method (iteratively) converges to the true distribution generated by the SDE.}, - author = {Ignace Bossuyt and Stefan Vandewalle and Giovanni Samaey}, - howpublished = {arXiv:2310.11365v1 [math.NA]}, - title = {Monte-Carlo/Moments micro-macro Parareal method for unimodal and bimodal scalar McKean-Vlasov SDEs}, - url = {http://arxiv.org/abs/2310.11365v1}, - year = {2023}, -} - @article{Cacciapuoti2023, author = {Luisa D{\textquotesingle}Amore and Rosalba Cacciapuoti}, doi = {10.4208/nmtma.oa-2022-0203}, @@ -6405,15 +6396,6 @@ @article{DajanaEtAl2023 year = {2023}, } -@unpublished{DanieliEtAl2023, - abstract = {This work develops a novel all-at-once space-time preconditioning approach for resistive magnetohydrodynamics (MHD), with a focus on model problems targeting fusion reactor design. We consider parallel-in-time due to the long time domains required to capture the physics of interest, as well as the complexity of the underlying system and thereby computational cost of long-time integration. To ameliorate this cost by using many processors, we thus develop a novel approach to solving the whole space-time system that is parallelizable in both space and time. We develop a space-time block preconditioning for resistive MHD, following the space-time block preconditioning concept first introduced by Danieli et al. in 2022 for incompressible flow, where an effective preconditioner for classic sequential time-stepping is extended to the space-time setting. The starting point for our derivation is the continuous Schur complement preconditioner by Cyr et al. in 2021, which we proceed to generalise in order to produce, to our knowledge, the first space-time block preconditioning approach for the challenging equations governing incompressible resistive MHD. The numerical results are promising for the model problems of island coalescence and tearing mode, with the overhead computational cost associated with space-time preconditioning versus sequential time-stepping being modest and primarily in the range of 2x-5x, which is low for parallel-in-time schemes in general. Additionally, the scaling results for inner (linear) and outer (nonlinear) iterations are flat in the case of fixed time-step size and only grow very slowly in the case of time-step refinement.}, - author = {Federico Danieli and Ben S. Southworth and Jacob B. Schroder}, - howpublished = {arXiv:2309.00768v1 [math.NA]}, - title = {Space-Time Block Preconditioning for Incompressible Resistive Magnetohydrodynamics}, - url = {http://arxiv.org/abs/2309.00768v1}, - year = {2023}, -} - @article{DrewsEtAl2023, author = {Drews, Wiebke and Turek, Stefan and Lohmann, Christoph}, doi = {10.17877/DE290R-23990}, @@ -6853,15 +6835,6 @@ @unpublished{ZhouEtAl2023b year = {2023}, } -@unpublished{AppelEtAl2024, - abstract = {This paper presents a method of performing topology optimisation of transient heat conduction problems using the parallel-in-time method Parareal. To accommodate the adjoint analysis, the Parareal method was modified to store intermediate time steps. Preliminary tests revealed that Parareal requires many iterations to achieve accurate results and, thus, achieves no appreciable speedup. To mitigate this, a one-shot approach was used, where the time history is iteratively refined over the optimisation process. The method estimates objectives and sensitivities by introducing cumulative objectives and sensitivities and solving for these using a single iteration of Parareal, after which it updates the design using the Method of Moving Asymptotes. The resulting method was applied to a test problem where a power mean of the temperature was minimised. It achieved a peak speedup relative to a sequential reference method of $5\times$ using 16 threads. The resulting designs were similar to the one found by the reference method, both in terms of objective values and qualitative appearance. The one-shot Parareal method was compared to the Parallel Local-in-Time method of topology optimisation. This revealed that the Parallel Local-in-Time method was unstable for the considered test problem, but it achieved a peak speedup of $12\times$ using 32 threads. It was determined that the dominant bottleneck in the one-shot Parareal method was the time spent on computing coarse propagators.}, - author = {Magnus Appel and Joe Alexandersen}, - howpublished = {arXiv:2411.19030v1 [cs.CE]}, - title = {One-shot Parareal Approach for Topology Optimisation of Transient Heat Flow}, - url = {http://arxiv.org/abs/2411.19030v1}, - year = {2024}, -} - @article{BaumannEtAl2024, abstract = {Spectral Deferred Correction (SDC) is an iterative method for the numerical solution of ordinary differential equations. It works by refining the numerical solution for an initial value problem by approximately solving differential equations for the error, and can be interpreted as a preconditioned fixed-point iteration for solving the fully implicit collocation problem. We adopt techniques from embedded Runge-Kutta Methods (RKM) to SDC in order to provide a mechanism for adaptive time step size selection and thus increase computational efficiency of SDC. We propose two SDC-specific estimates of the local error that are generic and do not rely on problem specific quantities. We demonstrate a gain in efficiency over standard SDC with fixed step size and compare efficiency favorably against state-of-the-art adaptive RKM.}, author = {Baumann, Thomas and G{\"o}tschel, Sebastian and Lunet, Thibaut and Ruprecht, Daniel and Speck, Robert}, @@ -6893,15 +6866,6 @@ @unpublished{BetckeEtAl2024 year = {2024}, } -@unpublished{BonteEtAl2024, - abstract = {Recently, the ParaOpt algorithm was proposed as an extension of the time-parallel Parareal method to optimal control. ParaOpt uses quasi-Newton steps that each require solving a system of matching conditions iteratively. The state-of-the-art parallel preconditioner for linear problems leads to a set of independent smaller systems that are currently hard to solve. We generalize the preconditioner to the nonlinear case and propose a new, fast inversion method for these smaller systems, avoiding disadvantages of the current options with adjusted boundary conditions in the subproblems.}, - author = {Corentin Bonte and Arne Bouillon and Giovanni Samaey and Karl Meerbergen}, - howpublished = {arXiv:2412.02425v1 [math.NA]}, - title = {Efficient parallel inversion of ParaOpt preconditioners}, - url = {http://arxiv.org/abs/2412.02425v1}, - year = {2024}, -} - @unpublished{BossuytEtAl2024, abstract = {In this paper, we are concerned with the micro-macro Parareal algorithm for the simulation of initial-value problems. In this algorithm, a coarse (fast) solver is applied sequentially over the time domain, and a fine (time-consuming) solver is applied as a corrector in parallel over smaller chunks of the time interval. Moreover, the coarse solver acts on a reduced state variable, which is coupled to the fine state variable through appropriate coupling operators. We first provide a contribution to the convergence analysis of the micro-macro Parareal method for multiscale linear ordinary differential equations (ODEs). Then, we extend a variant of the micro-macro Parareal algorithm for scalar stochastic differential equations (SDEs) to higher-dimensional SDEs.}, author = {Ignace Bossuyt and Stefan Vandewalle and Giovanni Samaey}, @@ -7063,15 +7027,6 @@ @article{FangEtAl2024 year = {2024}, } -@unpublished{FreeseEtAl2024, - abstract = {We investigate parallel performance of parallel spectral deferred corrections, a numerical approach that provides small-scale parallelism for the numerical solution of initial value problems. The scheme is applied to the shallow water equation and uses an IMEX splitting that integrates fast modes implicitly and slow modes explicitly in order to be efficient. We describe parallel $\texttt{OpenMP}$-based implementations of parallel SDC in two well established simulation codes: the finite volume based operational ocean model $\texttt{ICON-O}$ and the spherical harmonics based research code $\texttt{SWEET}$. The implementations are benchmarked on a single node of the JUSUF ($\texttt{SWEET}$) and JUWELS ($\texttt{ICON-O}$) system at J\"ulich Supercomputing Centre. We demonstrate a reduction of time-to-solution across a range of accuracies. For $\texttt{ICON-O}$, we show speedup over the currently used Adams--Bashforth-2 integrator with $\texttt{OpenMP}$ loop parallelization. For $\texttt{SWEET}$, we show speedup over serial spectral deferred corrections and a second order implicit-explicit integrator.}, - author = {Philip Freese and Sebastian Götschel and Thibaut Lunet and Daniel Ruprecht and Martin Schreiber}, - howpublished = {arXiv:2403.20135v1 [cs.CE]}, - title = {Parallel performance of shared memory parallel spectral deferred corrections}, - url = {http://arxiv.org/abs/2403.20135v1}, - year = {2024}, -} - @unpublished{FungEtAl2024, abstract = {In this work, we propose a class of novel preconditioned Krylov subspace methods for solving an optimal control problem of parabolic equations. Namely, we develop a family of block $\omega$-circulant based preconditioners for the all-at-once linear system arising from the concerned optimal control problem, where both first order and second order time discretization methods are considered. The proposed preconditioners can be efficiently diagonalized by fast Fourier transforms in a parallel-in-time fashion, and their effectiveness is theoretically shown in the sense that the eigenvalues of the preconditioned matrix are clustered around $\pm 1$, which leads to rapid convergence when the minimal residual method is used. When the generalized minimal residual method is deployed, the efficacy of the proposed preconditioners are justified in the way that the singular values of the preconditioned matrices are proven clustered around unity. Numerical results are provided to demonstrate the effectiveness of our proposed solvers.}, author = {Po Yin Fung and Sean Hon}, @@ -7145,21 +7100,16 @@ @article{GanglEtAl2024 year = {2024}, } -@unpublished{GattiglioEtAl2024, - abstract = {With the advent of supercomputers, multi-processor environments and parallel-in-time (PinT) algorithms offer ways to solve initial value problems for ordinary and partial differential equations (ODEs and PDEs) over long time intervals, a task often unfeasible with sequential solvers within realistic time frames. A recent approach, GParareal, combines Gaussian Processes with traditional PinT methodology (Parareal) to achieve faster parallel speed-ups. The method is known to outperform Parareal for low-dimensional ODEs and a limited number of computer cores. Here, we present Nearest Neighbors GParareal (nnGParareal), a novel data-enriched PinT integration algorithm. nnGParareal builds upon GParareal by improving its scalability properties for higher-dimensional systems and increased processor count. Through data reduction, the model complexity is reduced from cubic to log-linear in the sample size, yielding a fast and automated procedure to integrate initial value problems over long time intervals. First, we provide both an upper bound for the error and theoretical details on the speed-up benefits. Then, we empirically illustrate the superior performance of nnGParareal, compared to GParareal and Parareal, on nine different systems with unique features (e.g., stiff, chaotic, high-dimensional, or challenging-to-learn systems).}, - author = {Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino}, - howpublished = {arXiv:2405.12182v1 [stat.CO]}, - title = {Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers}, - url = {http://arxiv.org/abs/2405.12182v1}, - year = {2024}, -} - -@unpublished{GattiglioEtAl2024b, - abstract = {Parallel-in-time (PinT) techniques have been proposed to solve systems of time-dependent differential equations by parallelizing the temporal domain. Among them, Parareal computes the solution sequentially using an inaccurate (fast) solver, and then "corrects" it using an accurate (slow) integrator that runs in parallel across temporal subintervals. This work introduces RandNet-Parareal, a novel method to learn the discrepancy between the coarse and fine solutions using random neural networks (RandNets). RandNet-Parareal achieves speed gains up to x125 and x22 compared to the fine solver run serially and Parareal, respectively. Beyond theoretical guarantees of RandNets as universal approximators, these models are quick to train, allowing the PinT solution of partial differential equations on a spatial mesh of up to $10^5$ points with minimal overhead, dramatically increasing the scalability of existing PinT approaches. RandNet-Parareal's numerical performance is illustrated on systems of real-world significance, such as the viscous Burgers' equation, the Diffusion-Reaction equation, the two- and three-dimensional Brusselator, and the shallow water equation.}, - author = {Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino}, - howpublished = {arXiv:2411.06225v1 [stat.CO]}, +@inproceedings{GattiglioEtAl2024b, + author = {Gattiglio, Guglielmo and Grigoryeva, Lyudmila and Tamborrino, Massimiliano}, + booktitle = {Advances in Neural Information Processing Systems 37}, + collection = {NeurIPS 2024}, + doi = {10.52202/079017-3011}, + pages = {94993–95025}, + publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)}, + series = {NeurIPS 2024}, title = {RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks}, - url = {http://arxiv.org/abs/2411.06225v1}, + url = {http://dx.doi.org/10.52202/079017-3011}, year = {2024}, } @@ -7223,15 +7173,6 @@ @article{HeinkenschlossEtAl2024 year = {2024}, } -@unpublished{HeinzelreiterEtAl2024, - abstract = {We derive a new parallel-in-time approach for solving large-scale optimization problems constrained by time-dependent partial differential equations arising from fluid dynamics. The solver involves the use of a block circulant approximation of the original matrices, enabling parallelization-in-time via the use of fast Fourier transforms, and we devise bespoke matrix approximations which may be applied within this framework. These make use of permutations, saddle-point approximations, commutator arguments, as well as inner solvers such as the Uzawa method, Chebyshev semi-iteration, and multigrid. Theoretical results underpin our strategy of applying a block circulant strategy, and numerical experiments demonstrate the effectiveness and robustness of our approach on Stokes and Oseen problems. Noteably, satisfying results for the strong and weak scaling of our methods are provided within a fully parallel architecture.}, - author = {Bernhard Heinzelreiter and John W. Pearson}, - howpublished = {arXiv:2405.18964v1 [math.NA]}, - title = {Diagonalization-Based Parallel-in-Time Preconditioners for Instationary Fluid Flow Control Problems}, - url = {http://arxiv.org/abs/2405.18964v1}, - year = {2024}, -} - @unpublished{HonEtAl2024, abstract = {In this work, we propose a novel diagonalization-based preconditioner for the all-at-once linear system arising from the optimal control problem of parabolic equations. The proposed preconditioner is constructed based on an $\epsilon$-circulant modification to the rotated block diagonal (RBD) preconditioning technique, which can be efficiently diagonalized by fast Fourier transforms in a parallel-in-time fashion. To our knowledge, this marks the first application of the $\epsilon$-circulant modification to RBD preconditioning. Before our work, the studies of PinT preconditioning techniques for the optimal control problem are mainly focused on $\epsilon$-circulant modification to Schur complement based preconditioners, which involves multiplication of forward and backward evolutionary processes and thus square the condition number. Compared with those Schur complement based preconditioning techniques in the literature, the advantage of the proposed $\epsilon$-circulant modified RBD preconditioning is that it does not involve the multiplication of forward and backward evolutionary processes. When the generalized minimal residual method is deployed on the preconditioned system, we prove that when choosing $\epsilon=\mathcal{O}(\sqrt{\tau})$ with $\tau$ being the temporal step-size , the convergence rate of the preconditioned GMRES solver is independent of the matrix size and the regularization parameter. Such restriction on $\epsilon$ is more relax than the assumptions on $\epsilon$ from other works related to $\epsilon$-circulant based preconditioning techniques for the optimal control problem. Numerical results are provided to demonstrate the effectiveness of our proposed solvers.}, author = {Sean Y. Hon and Po Yin Fung and Xue-lei Lin}, @@ -7414,15 +7355,6 @@ @article{LiEtAl2024 year = {2024}, } -@unpublished{MardalEtAl2024, - abstract = {We consider a PDE-constrained optimization problem of tracking type with parabolic state equation. The solution to the problem is characterized by the Karush-Kuhn-Tucker (KKT) system, which we formulate using a strong variational formulation of the state equation and a super weak formulation of the adjoined state equation. This allows us to propose a preconditioner that is robust both in the regularization and the diffusion parameter. In order to discretize the problem, we use Isogeometric Analysis since it allows the construction of sufficiently smooth basis functions effortlessly. To realize the preconditioner, one has to solve a problem over the whole space time cylinder that is elliptic with respect to certain non-standard norms. Using a fast diagonalization approach in time, we reformulate the problem as a collection of elliptic problems in space only. These problems are not only smaller, but our approach also allows to solve them in a time-parallel way. We show the efficiency of the preconditioner by rigorous analysis and illustrate it with numerical experiments.}, - author = {Kent-Andre Mardal and Jarle Sogn and Stefan Takacs}, - howpublished = {arXiv:2407.17964v1 [math.NA]}, - title = {A robust and time-parallel preconditioner for parabolic reconstruction problems using Isogeometric Analysis}, - url = {http://arxiv.org/abs/2407.17964v1}, - year = {2024}, -} - @unpublished{MargenbergEtAl2024, abstract = {We present a space-time multigrid method based on tensor-product space-time finite element discretizations. The method is facilitated by the matrix-free capabilities of the {\ttfamily deal.II} library. It addresses both high-order continuous and discontinuous variational time discretizations with spatial finite element discretizations. The effectiveness of multigrid methods in large-scale stationary problems is well established. However, their application in the space-time context poses significant challenges, mainly due to the construction of suitable smoothers. To address these challenges, we develop a space-time cell-wise additive Schwarz smoother and demonstrate its effectiveness on the heat and acoustic wave equations. The matrix-free framework of the {\ttfamily deal.II} library supports various multigrid strategies, including $h$-, $p$-, and $hp$-refinement across spatial and temporal dimensions. Extensive empirical evidence, provided through scaling and convergence tests on high-performance computing platforms, demonstrate high performance on perturbed meshes and problems with heterogeneous and discontinuous coefficients. Throughputs of over a billion degrees of freedom per second are achieved on problems with more than a trillion global degrees of freedom. The results prove that the space-time multigrid method can effectively solve complex problems in high-fidelity simulations and show great potential for use in coupled problems.}, author = {Nils Margenberg and Peter Munch}, @@ -7548,12 +7480,16 @@ @unpublished{SchnaubeltEtAl2024 year = {2024}, } -@unpublished{SelvamEtAl2024, - abstract = {In diffusion models, samples are generated through an iterative refinement process, requiring hundreds of sequential model evaluations. Several recent methods have introduced approximations (fewer discretization steps or distillation) to trade off speed at the cost of sample quality. In contrast, we introduce Self-Refining Diffusion Samplers (SRDS) that retain sample quality and can improve latency at the cost of additional parallel compute. We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations. In SRDS, a quick but rough estimate of a sample is first created and then iteratively refined in parallel through Parareal iterations. SRDS is not only guaranteed to accurately solve the ODE and converge to the serial solution but also benefits from parallelization across the diffusion trajectory, enabling batched inference and pipelining. As we demonstrate for pre-trained diffusion models, the early convergence of this refinement procedure drastically reduces the number of steps required to produce a sample, speeding up generation for instance by up to 1.7x on a 25-step StableDiffusion-v2 benchmark and up to 4.3x on longer trajectories.}, - author = {Nikil Roashan Selvam and Amil Merchant and Stefano Ermon}, - howpublished = {arXiv:2412.08292v1 [cs.LG]}, +@inproceedings{SelvamEtAl2024, + author = {Ermon, Stefano and Merchant, Amil and Selvam, Nikil}, + booktitle = {Advances in Neural Information Processing Systems 37}, + collection = {NeurIPS 2024}, + doi = {10.52202/079017-0176}, + pages = {5429–5453}, + publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)}, + series = {NeurIPS 2024}, title = {Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations}, - url = {http://arxiv.org/abs/2412.08292v1}, + url = {http://dx.doi.org/10.52202/079017-0176}, year = {2024}, } @@ -7566,15 +7502,6 @@ @unpublished{SouzaEtAl2024 year = {2024}, } -@unpublished{SterckEtAl2024, - abstract = {We consider the parallel-in-time solution of scalar nonlinear conservation laws in one spatial dimension. The equations are discretized in space with a conservative finite-volume method using weighted essentially non-oscillatory (WENO) reconstructions, and in time with high-order explicit Runge-Kutta methods. The solution of the global, discretized space-time problem is sought via a nonlinear iteration that uses a novel linearization strategy in cases of non-differentiable equations. Under certain choices of discretization and algorithmic parameters, the nonlinear iteration coincides with Newton's method, although, more generally, it is a preconditioned residual correction scheme. At each nonlinear iteration, the linearized problem takes the form of a certain discretization of a linear conservation law over the space-time domain in question. An approximate parallel-in-time solution of the linearized problem is computed with a single multigrid reduction-in-time (MGRIT) iteration. The MGRIT iteration employs a novel coarse-grid operator that is a modified conservative semi-Lagrangian discretization and generalizes those we have developed previously for non-conservative scalar linear hyperbolic problems. Numerical tests are performed for the inviscid Burgers and Buckley--Leverett equations. For many test problems, the solver converges in just a handful of iterations with convergence rate independent of mesh resolution, including problems with (interacting) shocks and rarefactions.}, - author = {H. De Sterck and R. D. Falgout and O. A. Krzysik and J. B. Schroder}, - howpublished = {arXiv:2401.04936v1 [math.NA]}, - title = {Parallel-in-time solution of scalar nonlinear conservation laws}, - url = {http://arxiv.org/abs/2401.04936v1}, - year = {2024}, -} - @article{WuEtAl2024, author = {Wu, Shu-Lin and Zhou, Tao}, doi = {10.1137/23m1592481}, @@ -7681,12 +7608,18 @@ @article{AlesEtAl2025 year = {2025}, } -@unpublished{AlexandersenEtAl2025, - abstract = {This paper presents a novel space-time topology optimisation framework for time-dependent thermal conduction problems, aiming to significantly reduce the time-to-solution. By treating time as an additional spatial dimension, we discretise the governing equations using a stabilised continuous Galerkin space-time finite element method. The resulting large all-at-once system is solved using an iterative Krylov solver preconditioned with a parallel space-time multigrid method employing a semi-coarsening strategy. Implemented in a fully parallel computing framework, the method yields a parallel-in-time method that demonstrates excellent scalability on a distributed-memory supercomputer, solving problems up to 4.2 billion degrees of freedom. Comparative studies show up to 52x speed-up over traditional time-stepping approaches, with only moderate increases in total computational cost in terms of core-hours. The framework is validated on benchmark problems with both time-constant and time-varying designs, and its flexibility is demonstrated through variations in material properties. These results establish the proposed space-time method as a promising approach for large-scale time-dependent topology optimisation in thermal applications.}, - author = {Joe Alexandersen and Magnus Appel}, - howpublished = {arXiv:2508.09589v1 [cs.CE]}, - title = {Large-Scale Topology Optimisation of Time-dependent Thermal Conduction Using Space-Time Finite Elements and a Parallel Space-Time Multigrid Preconditioner}, - url = {http://arxiv.org/abs/2508.09589v1}, +@article{AppelEtAl2024, + author = {Appel, Magnus and Alexandersen, Joe}, + doi = {10.1137/24m1696603}, + issn = {1095-7197}, + journal = {SIAM Journal on Scientific Computing}, + month = {November}, + number = {6}, + pages = {B1450–B1480}, + publisher = {Society for Industrial & Applied Mathematics (SIAM)}, + title = {One-Shot Parareal Approach for Topology Optimization of Transient Heat Flow}, + url = {http://dx.doi.org/10.1137/24m1696603}, + volume = {47}, year = {2025}, } @@ -7763,6 +7696,21 @@ @article{BhattEtAl2025 year = {2025}, } +@article{BossuytEtAl2023, + author = {Bossuyt, Ignace and Vandewalle, Stefan and Samaey, Giovanni}, + doi = {10.1137/23m1609142}, + issn = {1095-7197}, + journal = {SIAM Journal on Scientific Computing}, + month = {November}, + number = {6}, + pages = {A3239–A3275}, + publisher = {Society for Industrial & Applied Mathematics (SIAM)}, + title = {Monte Carlo–Moments Micro-Macro Parareal Method for Unimodal and Bimodal Scalar McKean–Vlasov SDEs}, + url = {http://dx.doi.org/10.1137/23m1609142}, + volume = {47}, + year = {2025}, +} + @phdthesis{Bronasco2025, author = {{Bronasco, Ausra}}, doi = {10.13097/ARCHIVE-OUVERTE/UNIGE:187048}, @@ -7804,6 +7752,20 @@ @unpublished{DaiEtAl2025 year = {2025}, } +@article{DanieliEtAl2023, + author = {Danieli, Federico and Southworth, Ben S. and Schroder, Jacob B.}, + doi = {10.1002/nla.70034}, + issn = {1099-1506}, + journal = {Numerical Linear Algebra with Applications}, + month = {October}, + number = {6}, + publisher = {Wiley}, + title = {Space‐Time Block Preconditioning for Incompressible Resistive Magnetohydrodynamics}, + url = {http://dx.doi.org/10.1002/nla.70034}, + volume = {32}, + year = {2025}, +} + @inbook{DrewsEtAl2025, author = {Drews, Wiebke and Turek, Stefan and Lohmann, Christoph}, booktitle = {Numerical Mathematics and Advanced Applications ENUMATH 2023, Volume 1}, @@ -7817,15 +7779,6 @@ @inbook{DrewsEtAl2025 year = {2025}, } -@unpublished{DurastanteEtAl2025, - abstract = {Implicit Runge--Kutta (IRK) methods are highly effective for solving stiff ordinary differential equations (ODEs) but can be computationally expensive for large-scale problems due to the need of solving coupled algebraic equations at each step. This study improves IRK efficiency by leveraging parallelism to decouple stage computations and reduce communication overhead, specifically we stably decouple a perturbed version of the stage system of equations and recover the exact solution by solving a Sylvester matrix equation with an explicitly known low-rank right-hand side. Two IRK families -- symmetric methods and collocation methods -- are analyzed, with extensions to nonlinear problems using a simplified Newton method. Implementation details, shared memory parallel code, and numerical examples, particularly for ODEs from spatially discretized PDEs, demonstrate the efficiency of the proposed IRK technique.}, - author = {Fabio Durastante and Mariarosa Mazza}, - howpublished = {arXiv:2505.17719v1 [math.NA]}, - title = {Stage-Parallel Implicit Runge--Kutta methods via low-rank matrix equation corrections}, - url = {http://arxiv.org/abs/2505.17719v1}, - year = {2025}, -} - @unpublished{EggerEtAl2025, abstract = {A class of abstract nonlinear time-periodic evolution problems is considered which arise in electrical engineering and other scientific disciplines. An efficient solver is proposed for the systems arising after discretization in time based on a fixed-point iteration. Every step of this iteration amounts to the solution of a discretized time-periodic and time-invariant problem for which efficient parallel-in-time methods are available. Global convergence with contraction factors independent of the discretization parameters is established. Together with an appropriate initialization step, a highly efficient and reliable solver is obtained. The applicability and performance of the proposed method is illustrated by simulations of a power transformer. Further comparison is made with other solution strategies proposed in the literature.}, author = {Herbert Egger and Andreas Schafelner}, @@ -7848,15 +7801,6 @@ @inbook{EndtmayerEtAl2025 year = {2025}, } -@unpublished{EngwerEtAl2025, - abstract = {High order methods have shown great potential to overcome performance issues of simulations of partial differential equations (PDEs) on modern hardware, still many users stick to low-order, matrixbased simulations, in particular in porous media applications. Heterogeneous coefficients and low regularity of the solution are reasons not to employ high order discretizations. We present a new approach for the simulation of instationary PDEs that allows to partially mitigate the performance problems. By reformulating the original problem we derive a parallel in time time integrator that increases the arithmetic intensity and introduces additional structure into the problem. By this it helps accelerate matrix-based simulations on modern hardware architectures. Based on a system for multiple time steps we will formulate a matrix equation that can be solved using vectorised solvers like Block Krylov methods. The structure of this approach makes it applicable for a wide range of linear and nonlinear problems. In our numerical experiments we present some first results for three different PDEs, a linear convection-diffusion equation, a nonlinear diffusion-reaction equation and a realistic example based on the Richards' equation.}, - author = {Christian Engwer and Alexander Schell and Nils-Arne Dreier}, - howpublished = {arXiv:2504.02117v1 [math.NA]}, - title = {Vectorised Parallel in Time methods for low-order discretizations with application to Porous Media problems}, - url = {http://arxiv.org/abs/2504.02117v1}, - year = {2025}, -} - @techreport{FalgoutSchroder2025, author = {Falgout, Robert D. and Schroder, Jacob B.}, institution = {Lawrence Livermore National Laboratory}, @@ -7880,6 +7824,18 @@ @article{FeketeEtAl2025 year = {2025}, } +@article{FreeseEtAl2024, + author = {Freese, Philip and Götschel, Sebastian and Lunet, Thibaut and Ruprecht, Daniel and Schreiber, Martin}, + doi = {10.1177/10943420251400406}, + issn = {1741-2846}, + journal = {The International Journal of High Performance Computing Applications}, + month = {December}, + publisher = {SAGE Publications}, + title = {Parallel performance of shared memory parallel spectral deferred corrections}, + url = {http://dx.doi.org/10.1177/10943420251400406}, + year = {2025}, +} + @article{FungEtAl2025, author = {Fung, Po Yin and Hon, Sean Y.}, doi = {10.1016/j.camwa.2025.01.019}, @@ -7927,6 +7883,21 @@ @unpublished{GanderEtAl2025b year = {2025}, } +@article{GattiglioEtAl2024, + author = {Gattiglio, Guglielmo and Grigoryeva, Lyudmila and Tamborrino, Massimiliano}, + doi = {10.1137/24m1663648}, + issn = {1095-7197}, + journal = {SIAM Journal on Scientific Computing}, + month = {November}, + number = {6}, + pages = {B1400–B1423}, + publisher = {Society for Industrial & Applied Mathematics (SIAM)}, + title = {Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers}, + url = {http://dx.doi.org/10.1137/24m1663648}, + volume = {47}, + year = {2025}, +} + @unpublished{GattiglioEtAl2025, abstract = {We introduce Prob-GParareal, a probabilistic extension of the GParareal algorithm designed to provide uncertainty quantification for the Parallel-in-Time (PinT) solution of (ordinary and partial) differential equations (ODEs, PDEs). The method employs Gaussian processes (GPs) to model the Parareal correction function, as GParareal does, further enabling the propagation of numerical uncertainty across time and yielding probabilistic forecasts of system's evolution. Furthermore, Prob-GParareal accommodates probabilistic initial conditions and maintains compatibility with classical numerical solvers, ensuring its straightforward integration into existing Parareal frameworks. Here, we first conduct a theoretical analysis of the computational complexity and derive error bounds of Prob-GParareal. Then, we numerically demonstrate the accuracy and robustness of the proposed algorithm on five benchmark ODE systems, including chaotic, stiff, and bifurcation problems. To showcase the flexibility and potential scalability of the proposed algorithm, we also consider Prob-nnGParareal, a variant obtained by replacing the GPs in Parareal with the nearest-neighbors GPs, illustrating its increased performance on an additional PDE example. This work bridges a critical gap in the development of probabilistic counterparts to established PinT methods.}, author = {Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino}, @@ -7963,15 +7934,6 @@ @unpublished{GuEtAl2025 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}, - howpublished = {arXiv:2507.19246v1 [cs.CE]}, - title = {Multi-Level Monte Carlo sampling with Parallel-in-Time Integration for Uncertainty Quantification in Electric Machine Simulation}, - url = {http://arxiv.org/abs/2507.19246v1}, - year = {2025}, -} - @inproceedings{HamdanEtAl2025, author = {Hamdan, Juman and Riahi, Mohamed Kamel}, booktitle = {Journal of Physics: Conference Series}, @@ -7983,6 +7945,18 @@ @inproceedings{HamdanEtAl2025 year = {2025}, } +@article{HeinzelreiterEtAl2024, + author = {Heinzelreiter, Bernhard and Pearson, John W}, + doi = {10.1093/imanum/draf088}, + issn = {1464-3642}, + journal = {IMA Journal of Numerical Analysis}, + month = {November}, + publisher = {Oxford University Press (OUP)}, + title = {Diagonalization-based parallel-in-time preconditioners for instationary fluid flow control problems}, + url = {http://dx.doi.org/10.1093/imanum/draf088}, + year = {2025}, +} + @article{HeinzelreiterEtAl2025, author = {Heinzelreiter, Bernhard and Pearson, John W}, doi = {10.1093/imanum/draf088}, @@ -8340,6 +8314,21 @@ @article{SperryEtAl2025 year = {2025}, } +@article{SterckEtAl2024, + author = {Krzysik, O. A. and De Sterck, H. and Falgout, R. D. and Schroder, J. B.}, + doi = {10.1137/24m1630268}, + issn = {1095-7197}, + journal = {SIAM Journal on Scientific Computing}, + month = {November}, + number = {6}, + pages = {A3134–A3160}, + publisher = {Society for Industrial & Applied Mathematics (SIAM)}, + title = {Parallel-in-Time Solution of Scalar Nonlinear Conservation Laws}, + url = {http://dx.doi.org/10.1137/24m1630268}, + volume = {47}, + year = {2025}, +} + @article{StumpEtAl2025, author = {Stump, Benjamin C. and Arndt, Daniel and Rolchigo, Matt and Reeve, Samuel Temple}, doi = {10.1016/j.commatsci.2025.113684}, @@ -8519,6 +8508,20 @@ @unpublished{ZoltowskiEtAl2025 year = {2025}, } +@article{AlexandersenEtAl2025, + author = {Alexandersen, Joe and Appel, Magnus}, + doi = {10.1016/j.cma.2025.118605}, + issn = {0045-7825}, + journal = {Computer Methods in Applied Mechanics and Engineering}, + month = {March}, + pages = {118605}, + publisher = {Elsevier BV}, + title = {Large-scale topology optimisation of time-dependent thermal conduction using space-time finite elements and a parallel space-time multigrid preconditioner}, + url = {http://dx.doi.org/10.1016/j.cma.2025.118605}, + volume = {450}, + year = {2026}, +} + @article{AlexandersenEtAl2026, author = {Alexandersen, Joe and Appel, Magnus}, doi = {10.1016/j.cma.2025.118605}, @@ -8547,6 +8550,20 @@ @article{AluthgeEtAl2026 year = {2026}, } +@article{BonteEtAl2024, + author = {Bonte, Corentin and Bouillon, Arne and Samaey, Giovanni and Meerbergen, Karl}, + doi = {10.1016/j.cam.2026.117339}, + issn = {0377-0427}, + journal = {Journal of Computational and Applied Mathematics}, + month = {August}, + pages = {117339}, + publisher = {Elsevier BV}, + title = {Efficient parallel inversion of ParaOpt preconditioners}, + url = {http://dx.doi.org/10.1016/j.cam.2026.117339}, + volume = {482}, + year = {2026}, +} + @unpublished{DaiEtAl2026, abstract = {In this paper, we propose a model order reduction based adaptive parareal method for time-dependent partial differential equations. By using the data obtained by the fine propagator in each iteration of the plain parareal method together with some model order reduction technique, we construct the coarse propagator adaptively in each parareal iteration, and then obtain our adaptive parareal method. We apply this new method to solve some 3D time-dependent advection-diffusion equations with the Kolmogorov flow and the ABC flow. Numerical results show the good performance of our method in simulating long-term evolution problems.}, author = {Xiaoying Dai and Miao Hu and Shuwei Shen}, @@ -8556,6 +8573,20 @@ @unpublished{DaiEtAl2026 year = {2026}, } +@article{DurastanteEtAl2025, + author = {Durastante, Fabio and Mazza, Mariarosa}, + doi = {10.1007/s10915-026-03185-z}, + issn = {1573-7691}, + journal = {Journal of Scientific Computing}, + month = {February}, + number = {1}, + publisher = {Springer Science and Business Media LLC}, + title = {Stage-Parallel Implicit Runge–Kutta Methods Via Low-Rank Matrix Equation Corrections}, + url = {http://dx.doi.org/10.1007/s10915-026-03185-z}, + volume = {107}, + year = {2026}, +} + @article{DurastanteEtAl2026, author = {Durastante, Fabio and Mazza, Mariarosa}, doi = {10.1007/s10915-026-03185-z}, @@ -8570,6 +8601,20 @@ @article{DurastanteEtAl2026 year = {2026}, } +@article{EngwerEtAl2025, + author = {Engwer, Christian and Schell, Alexander and Dreier, Nils-Arne}, + doi = {10.1007/s13137-025-00283-2}, + issn = {1869-2680}, + journal = {GEM - International Journal on Geomathematics}, + month = {March}, + number = {1}, + publisher = {Springer Science and Business Media LLC}, + title = {Vectorized Parallel in Time methods for low-order discretizations with application to Porous Media problems}, + url = {http://dx.doi.org/10.1007/s13137-025-00283-2}, + volume = {17}, + year = {2026}, +} + @article{GaraiEtAl2026, author = {Garai, Gobinda and Mandal, Bankim C.}, doi = {10.1080/00207160.2026.2614077}, @@ -8592,6 +8637,18 @@ @unpublished{GaraiEtAl2026b year = {2026}, } +@article{HahnEtAl2025, + author = {Hahn, Robert and Schöps, Sebastian}, + doi = {10.1109/tmag.2026.3651851}, + issn = {1941-0069}, + journal = {IEEE Transactions on Magnetics}, + pages = {1–1}, + publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, + title = {Multi-Level Monte Carlo sampling with Parallel-in-Time Integration for Uncertainty Quantification in Electric Machine Simulation}, + url = {http://dx.doi.org/10.1109/tmag.2026.3651851}, + year = {2026}, +} + @article{HeEtAl2026, author = {He, Tingting and Zhai, Tianle and Huang, Xuhang and Li, Min}, doi = {10.1016/j.cnsns.2025.109183}, @@ -8642,6 +8699,21 @@ @unpublished{LuEtAl2026 year = {2026}, } +@article{MardalEtAl2024, + author = {Mardal, Kent-Andre and Sogn, Jarle and Takacs, Stefan}, + doi = {10.1142/s0218202526500168}, + issn = {1793-6314}, + journal = {Mathematical Models and Methods in Applied Sciences}, + month = {February}, + number = {04}, + pages = {861–886}, + publisher = {World Scientific Pub Co Pte Ltd}, + title = {A robust and time-parallel preconditioner for parabolic reconstruction problems using Isogeometric analysis}, + url = {http://dx.doi.org/10.1142/s0218202526500168}, + volume = {36}, + year = {2026}, +} + @unpublished{MargenbergEtAl2026, abstract = {We present a monolithic hp space-time multigrid method (hp-STMG) for tensor-product space-time finite element discretizations of the incompressible Navier-Stokes equations. We employ mapped inf-sup stable pairs $\mathbb Q_{r+1}/\mathbb P_{r}^{\mathrm{disc}}$ in space and a slabwise discontinuous Galerkin DG($k$) discretization in time. The resulting fully coupled nonlinear systems are solved by Newton-GMRES preconditioned with hp-STMG, combining geometric coarsening in space with polynomial coarsening in space and time. Our main contribution is an hp-robust and practically efficient extension of space-time multigrid to Navier-Stokes: matrix-free operator evaluation is retained via column-wise, state-dependent spatial kernels; the nonlinear convective term is handled by a reduced, order-preserving time quadrature. Robustness is ensured by an inexact space-time Vanka smoother based on patch models with single time point evaluation. The method is implemented in the matrix-free multigrid framework of deal.II and demonstrates h- and p-robust convergence with robust solver performance across a range of Reynolds numbers, as well as high throughput in large-scale MPI-parallel experiments.}, author = {Nils Margenberg and Markus Bause}, From 0a5ce9ac2abf5ebcb81656dd46435108ca925120 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 16 Mar 2026 05:58:08 +0000 Subject: [PATCH 2/3] Initial plan From cac5fcc9f25891e6d38ebb9ab99846b0b3069af4 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 16 Mar 2026 06:07:59 +0000 Subject: [PATCH 3/3] Update arxiv_to_publications_correct.py to use publication year in keys and fix pint.bib entries Co-authored-by: pancetta <7158893+pancetta@users.noreply.github.com> --- _bibliography/pint.bib | 137 ++++++++------------------- bin/arxiv_to_publications_correct.py | 18 +++- 2 files changed, 56 insertions(+), 99 deletions(-) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index 4af3c9ac..b8b7d5b1 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -7012,6 +7012,19 @@ @article{EndtmayerEtAl2024 year = {2024}, } +@inproceedings{ErmonEtAl2024, + author = {Ermon, Stefano and Merchant, Amil and Selvam, Nikil}, + booktitle = {Advances in Neural Information Processing Systems 37}, + collection = {NeurIPS 2024}, + doi = {10.52202/079017-0176}, + pages = {5429–5453}, + publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)}, + series = {NeurIPS 2024}, + title = {Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations}, + url = {http://dx.doi.org/10.52202/079017-0176}, + year = {2024}, +} + @article{FangEtAl2024, author = {Fang, Rui and Tsai, Richard}, doi = {10.1007/s11075-024-01826-8}, @@ -7100,7 +7113,7 @@ @article{GanglEtAl2024 year = {2024}, } -@inproceedings{GattiglioEtAl2024b, +@inproceedings{GattiglioEtAl2024, author = {Gattiglio, Guglielmo and Grigoryeva, Lyudmila and Tamborrino, Massimiliano}, booktitle = {Advances in Neural Information Processing Systems 37}, collection = {NeurIPS 2024}, @@ -7480,19 +7493,6 @@ @unpublished{SchnaubeltEtAl2024 year = {2024}, } -@inproceedings{SelvamEtAl2024, - author = {Ermon, Stefano and Merchant, Amil and Selvam, Nikil}, - booktitle = {Advances in Neural Information Processing Systems 37}, - collection = {NeurIPS 2024}, - doi = {10.52202/079017-0176}, - pages = {5429–5453}, - publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)}, - series = {NeurIPS 2024}, - title = {Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations}, - url = {http://dx.doi.org/10.52202/079017-0176}, - year = {2024}, -} - @unpublished{SouzaEtAl2024, abstract = {Simulation of the monodomain equation, crucial for modeling the heart's electrical activity, faces scalability limits when traditional numerical methods only parallelize in space. To optimize the use of large multi-processor computers by distributing the computational load more effectively, time parallelization is essential. We introduce a high-order parallel-in-time method addressing the substantial computational challenges posed by the stiff, multiscale, and nonlinear nature of cardiac dynamics. Our method combines the semi-implicit and exponential spectral deferred correction methods, yielding a hybrid method that is extended to parallel-in-time employing the PFASST framework. We thoroughly evaluate the stability, accuracy, and robustness of the proposed parallel-in-time method through extensive numerical experiments, using practical ionic models such as the ten-Tusscher-Panfilov. The results underscore the method's potential to significantly enhance real-time and high-fidelity simulations in biomedical research and clinical applications.}, author = {Giacomo Rosilho de Souza and Simone Pezzuto and Rolf Krause}, @@ -7608,7 +7608,16 @@ @article{AlesEtAl2025 year = {2025}, } -@article{AppelEtAl2024, +@article{AppelEtAl2025, + author = {Appel, Magnus and Alexandersen, Joe}, + doi = {10.2139/ssrn.5256438}, + publisher = {Elsevier BV}, + title = {Space-Time Multigrid Methods Suitable for Topology Optimisation of Transient Heat Conduction}, + url = {http://dx.doi.org/10.2139/ssrn.5256438}, + year = {2025}, +} + +@article{AppelEtAl2025b, author = {Appel, Magnus and Alexandersen, Joe}, doi = {10.1137/24m1696603}, issn = {1095-7197}, @@ -7623,15 +7632,6 @@ @article{AppelEtAl2024 year = {2025}, } -@article{AppelEtAl2025, - author = {Appel, Magnus and Alexandersen, Joe}, - doi = {10.2139/ssrn.5256438}, - publisher = {Elsevier BV}, - title = {Space-Time Multigrid Methods Suitable for Topology Optimisation of Transient Heat Conduction}, - url = {http://dx.doi.org/10.2139/ssrn.5256438}, - year = {2025}, -} - @unpublished{ArrarasEtAl2025, abstract = {In view of the existing limitations of sequential computing, parallelization has emerged as an alternative in order to improve the speedup of numerical simulations. In the framework of evolutionary problems, space-time parallel methods offer the possibility to optimize parallelization. In the present paper, we propose a new family of these methods, built as a combination of the well-known parareal algorithm and suitable splitting techniques which permit us to parallelize in space. In particular, dimensional and domain decomposition splittings are considered for partitioning the elliptic operator, and first-order splitting time integrators are chosen as the propagators of the parareal algorithm to solve the resulting split problem. The major contribution of these methods is that, not only does the fine propagator perform in parallel, but also the coarse propagator. Unlike the classical version of the parareal algorithm, where all processors remain idle during the coarse propagator computations, the newly proposed schemes utilize the computational cores for both integrators. A convergence analysis of the methods is provided, and several numerical experiments are performed to test the solvers under consideration.}, author = {Andrés Arrarás and Francisco J. Gaspar and Iñigo Jimenez-Ciga and Laura Portero}, @@ -7696,7 +7696,7 @@ @article{BhattEtAl2025 year = {2025}, } -@article{BossuytEtAl2023, +@article{BossuytEtAl2025, author = {Bossuyt, Ignace and Vandewalle, Stefan and Samaey, Giovanni}, doi = {10.1137/23m1609142}, issn = {1095-7197}, @@ -7752,7 +7752,7 @@ @unpublished{DaiEtAl2025 year = {2025}, } -@article{DanieliEtAl2023, +@article{DanieliEtAl2025, author = {Danieli, Federico and Southworth, Ben S. and Schroder, Jacob B.}, doi = {10.1002/nla.70034}, issn = {1099-1506}, @@ -7824,7 +7824,7 @@ @article{FeketeEtAl2025 year = {2025}, } -@article{FreeseEtAl2024, +@article{FreeseEtAl2025, author = {Freese, Philip and Götschel, Sebastian and Lunet, Thibaut and Ruprecht, Daniel and Schreiber, Martin}, doi = {10.1177/10943420251400406}, issn = {1741-2846}, @@ -7883,7 +7883,16 @@ @unpublished{GanderEtAl2025b year = {2025}, } -@article{GattiglioEtAl2024, +@unpublished{GattiglioEtAl2025, + abstract = {We introduce Prob-GParareal, a probabilistic extension of the GParareal algorithm designed to provide uncertainty quantification for the Parallel-in-Time (PinT) solution of (ordinary and partial) differential equations (ODEs, PDEs). The method employs Gaussian processes (GPs) to model the Parareal correction function, as GParareal does, further enabling the propagation of numerical uncertainty across time and yielding probabilistic forecasts of system's evolution. Furthermore, Prob-GParareal accommodates probabilistic initial conditions and maintains compatibility with classical numerical solvers, ensuring its straightforward integration into existing Parareal frameworks. Here, we first conduct a theoretical analysis of the computational complexity and derive error bounds of Prob-GParareal. Then, we numerically demonstrate the accuracy and robustness of the proposed algorithm on five benchmark ODE systems, including chaotic, stiff, and bifurcation problems. To showcase the flexibility and potential scalability of the proposed algorithm, we also consider Prob-nnGParareal, a variant obtained by replacing the GPs in Parareal with the nearest-neighbors GPs, illustrating its increased performance on an additional PDE example. This work bridges a critical gap in the development of probabilistic counterparts to established PinT methods.}, + author = {Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino}, + howpublished = {arXiv:2509.03945v1 [stat.CO]}, + title = {Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations}, + url = {http://arxiv.org/abs/2509.03945v1}, + year = {2025}, +} + +@article{GattiglioEtAl2025b, author = {Gattiglio, Guglielmo and Grigoryeva, Lyudmila and Tamborrino, Massimiliano}, doi = {10.1137/24m1663648}, issn = {1095-7197}, @@ -7898,15 +7907,6 @@ @article{GattiglioEtAl2024 year = {2025}, } -@unpublished{GattiglioEtAl2025, - abstract = {We introduce Prob-GParareal, a probabilistic extension of the GParareal algorithm designed to provide uncertainty quantification for the Parallel-in-Time (PinT) solution of (ordinary and partial) differential equations (ODEs, PDEs). The method employs Gaussian processes (GPs) to model the Parareal correction function, as GParareal does, further enabling the propagation of numerical uncertainty across time and yielding probabilistic forecasts of system's evolution. Furthermore, Prob-GParareal accommodates probabilistic initial conditions and maintains compatibility with classical numerical solvers, ensuring its straightforward integration into existing Parareal frameworks. Here, we first conduct a theoretical analysis of the computational complexity and derive error bounds of Prob-GParareal. Then, we numerically demonstrate the accuracy and robustness of the proposed algorithm on five benchmark ODE systems, including chaotic, stiff, and bifurcation problems. To showcase the flexibility and potential scalability of the proposed algorithm, we also consider Prob-nnGParareal, a variant obtained by replacing the GPs in Parareal with the nearest-neighbors GPs, illustrating its increased performance on an additional PDE example. This work bridges a critical gap in the development of probabilistic counterparts to established PinT methods.}, - author = {Guglielmo Gattiglio and Lyudmila Grigoryeva and Massimiliano Tamborrino}, - howpublished = {arXiv:2509.03945v1 [stat.CO]}, - title = {Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations}, - url = {http://arxiv.org/abs/2509.03945v1}, - year = {2025}, -} - @unpublished{GengEtAl2025, abstract = {While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned solution as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep learning into the Parareal method requires consistency between the coarse and fine solvers, particularly for PDEs exhibiting rapid changes such as sharp transitions. To ensure such consistency, we propose to use the convolutional neural networks (CNNs) to learn the fully discrete time-stepping operator defined by the traditional numerical scheme used as the fine solver. We demonstrate the effectiveness of the proposed method in solving the classical and mass-conservative Allen-Cahn (AC) equations. Through iterative updates in the Parareal algorithm, our approach achieves a significant computational speedup compared to traditional fine solvers while converging to high-accuracy solutions. Our results highlight that the proposed Parareal algorithm effectively accelerates simulations, particularly when implemented on multiple GPUs, and converges to the desired accuracy in only a few iterations. Another advantage of our method is that the CNNs model is trained on trajectories-based on random initial conditions, such that the trained model can be used to solve the AC equations with various initial conditions without re-training. This work demonstrates the potential of integrating neural network methods into the parallel-in-time frameworks for efficient and accurate simulations of time-dependent PDEs.}, author = {Yuwei Geng and Junqi Yin and Eric C. Cyr and Guannan Zhang and Lili Ju}, @@ -7945,18 +7945,6 @@ @inproceedings{HamdanEtAl2025 year = {2025}, } -@article{HeinzelreiterEtAl2024, - author = {Heinzelreiter, Bernhard and Pearson, John W}, - doi = {10.1093/imanum/draf088}, - issn = {1464-3642}, - journal = {IMA Journal of Numerical Analysis}, - month = {November}, - publisher = {Oxford University Press (OUP)}, - title = {Diagonalization-based parallel-in-time preconditioners for instationary fluid flow control problems}, - url = {http://dx.doi.org/10.1093/imanum/draf088}, - year = {2025}, -} - @article{HeinzelreiterEtAl2025, author = {Heinzelreiter, Bernhard and Pearson, John W}, doi = {10.1093/imanum/draf088}, @@ -8314,21 +8302,6 @@ @article{SperryEtAl2025 year = {2025}, } -@article{SterckEtAl2024, - author = {Krzysik, O. A. and De Sterck, H. and Falgout, R. D. and Schroder, J. B.}, - doi = {10.1137/24m1630268}, - issn = {1095-7197}, - journal = {SIAM Journal on Scientific Computing}, - month = {November}, - number = {6}, - pages = {A3134–A3160}, - publisher = {Society for Industrial & Applied Mathematics (SIAM)}, - title = {Parallel-in-Time Solution of Scalar Nonlinear Conservation Laws}, - url = {http://dx.doi.org/10.1137/24m1630268}, - volume = {47}, - year = {2025}, -} - @article{StumpEtAl2025, author = {Stump, Benjamin C. and Arndt, Daniel and Rolchigo, Matt and Reeve, Samuel Temple}, doi = {10.1016/j.commatsci.2025.113684}, @@ -8508,20 +8481,6 @@ @unpublished{ZoltowskiEtAl2025 year = {2025}, } -@article{AlexandersenEtAl2025, - author = {Alexandersen, Joe and Appel, Magnus}, - doi = {10.1016/j.cma.2025.118605}, - issn = {0045-7825}, - journal = {Computer Methods in Applied Mechanics and Engineering}, - month = {March}, - pages = {118605}, - publisher = {Elsevier BV}, - title = {Large-scale topology optimisation of time-dependent thermal conduction using space-time finite elements and a parallel space-time multigrid preconditioner}, - url = {http://dx.doi.org/10.1016/j.cma.2025.118605}, - volume = {450}, - year = {2026}, -} - @article{AlexandersenEtAl2026, author = {Alexandersen, Joe and Appel, Magnus}, doi = {10.1016/j.cma.2025.118605}, @@ -8550,7 +8509,7 @@ @article{AluthgeEtAl2026 year = {2026}, } -@article{BonteEtAl2024, +@article{BonteEtAl2026, author = {Bonte, Corentin and Bouillon, Arne and Samaey, Giovanni and Meerbergen, Karl}, doi = {10.1016/j.cam.2026.117339}, issn = {0377-0427}, @@ -8573,20 +8532,6 @@ @unpublished{DaiEtAl2026 year = {2026}, } -@article{DurastanteEtAl2025, - author = {Durastante, Fabio and Mazza, Mariarosa}, - doi = {10.1007/s10915-026-03185-z}, - issn = {1573-7691}, - journal = {Journal of Scientific Computing}, - month = {February}, - number = {1}, - publisher = {Springer Science and Business Media LLC}, - title = {Stage-Parallel Implicit Runge–Kutta Methods Via Low-Rank Matrix Equation Corrections}, - url = {http://dx.doi.org/10.1007/s10915-026-03185-z}, - volume = {107}, - year = {2026}, -} - @article{DurastanteEtAl2026, author = {Durastante, Fabio and Mazza, Mariarosa}, doi = {10.1007/s10915-026-03185-z}, @@ -8601,7 +8546,7 @@ @article{DurastanteEtAl2026 year = {2026}, } -@article{EngwerEtAl2025, +@article{EngwerEtAl2026, author = {Engwer, Christian and Schell, Alexander and Dreier, Nils-Arne}, doi = {10.1007/s13137-025-00283-2}, issn = {1869-2680}, @@ -8637,7 +8582,7 @@ @unpublished{GaraiEtAl2026b year = {2026}, } -@article{HahnEtAl2025, +@article{HahnEtAl2026, author = {Hahn, Robert and Schöps, Sebastian}, doi = {10.1109/tmag.2026.3651851}, issn = {1941-0069}, @@ -8699,7 +8644,7 @@ @unpublished{LuEtAl2026 year = {2026}, } -@article{MardalEtAl2024, +@article{MardalEtAl2026, author = {Mardal, Kent-Andre and Sogn, Jarle and Takacs, Stefan}, doi = {10.1142/s0218202526500168}, issn = {1793-6314}, diff --git a/bin/arxiv_to_publications_correct.py b/bin/arxiv_to_publications_correct.py index b0ebff05..40ff8fc4 100644 --- a/bin/arxiv_to_publications_correct.py +++ b/bin/arxiv_to_publications_correct.py @@ -36,16 +36,28 @@ id = data['author'][0]['family'] + 'EtAl' + str(data['issued']['date-parts'][0][0]) else: id = data['author'][0]['family'] + str(data['issued']['date-parts'][0][0]) - if id != id_db: - print(f'Note: ID generated with new DOI ({id}) differs from the original in database ({id_db}). Keeping original ID.') + id = id.replace(" ", "_") entries = db.get_entry_dict() assert entries[id_db]["ENTRYTYPE"] == 'unpublished', "original entry in bib file was NOT unpublished !" db.entries.remove(entries[id_db]) + # Check for duplicate keys in the remaining database and add letter suffixes if needed + remaining = db.get_entry_dict() + id_orig = id + letters = 'bcdefghijklmnopqrstuvwxyz' + i = 0 + while id in remaining: + print(f'Key {id} already exists, augmenting with letter suffix.') + id = id_orig + letters[i] + i += 1 + + if id != id_db: + print(f'Note: ID updated from {id_db} to {id} to reflect the publication year.') + bType, *rest1 = bib.split("{") oldID, *rest2 = rest1[0].split(",") - bib = "{".join([bType] + [','.join([id_db]+rest2)] + rest1[1:]) + bib = "{".join([bType] + [','.join([id]+rest2)] + rest1[1:]) bib_db = bibtexparser.loads(bib) db.entries.extend(bib_db.get_entry_list())