References

This list of publications closely related to parallel-in-time integration is probably not complete. Please feel free to add any missing publications through a pull request on GitHub .

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2024

  1. M. Appel and J. Alexandersen, “One-shot Parareal Approach for Topology Optimisation of Transient Heat Flow,” arXiv:2411.19030v1 [cs.CE], 2024 [Online]. Available at: http://arxiv.org/abs/2411.19030v1
  2. M. M. Betcke, L. M. Kreusser, and D. Murari, “Parallel-in-Time Solutions with Random Projection Neural Networks,” arXiv:2408.09756v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2408.09756v1
  3. I. Bossuyt, S. Vandewalle, and G. Samaey, “Micro-macro Parareal, from ODEs to SDEs and back again,” arXiv:2401.01798v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2401.01798v1
  4. R. Cao, S. Hou, and L. Ma, “A Pipeline-Based ODE Solving Framework,” IEEE Access, vol. 12, pp. 37995–38004, 2024, doi: 10.1109/ACCESS.2024.3375305.
  5. P. Freese, S. Götschel, T. Lunet, D. Ruprecht, and M. Schreiber, “Parallel performance of shared memory parallel spectral deferred corrections,” arXiv:2403.20135v1 [cs.CE], 2024 [Online]. Available at: http://arxiv.org/abs/2403.20135v1
  6. P. Y. Fung and S. Hon, “Block ω-circulant preconditioners for parabolic optimal control problems,” arXiv:2406.00952v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2406.00952v1
  7. P. Y. Fung and S. Hon, “Block \(\boldsymbolω\)-Circulant Preconditioners for Parabolic Optimal Control Problems,” SIAM Journal on Matrix Analysis and Applications, vol. 45, no. 4, pp. 2263–2286, Dec. 2024, doi: 10.1137/23m1601432. [Online]. Available at: http://dx.doi.org/10.1137/23M1601432
  8. M. J. Gander, M. Ohlberger, and S. Rave, “A Parareal algorithm without Coarse Propagator?,” arXiv:2409.02673v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2409.02673v1
  9. G. Gattiglio, L. Grigoryeva, and M. Tamborrino, “Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers,” arXiv:2405.12182v1 [stat.CO], 2024 [Online]. Available at: http://arxiv.org/abs/2405.12182v1
  10. G. Gattiglio, L. Grigoryeva, and M. Tamborrino, “RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks,” arXiv:2411.06225v1 [stat.CO], 2024 [Online]. Available at: http://arxiv.org/abs/2411.06225v1
  11. X.-M. Gu, J. Liu, and C. W. Oosterlee, “Parallel-in-Time Iterative Methods for Pricing American Options,” arXiv:2405.08280v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2405.08280v1
  12. H. Gu, M. Cai, and J. Li, “Crank-Nicolson-type iterative decoupled algorithms for Biot’s consolidation model using total pressure,” arXiv:2409.18391v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2409.18391v1
  13. T. He, J. Lu, and M. Li, “The parareal algorithm for Caputo-Hadamard fractional differential equations,” Communications on Analysis and Computation, vol. 0, no. 0, pp. 0–0, 2024, doi: 10.3934/cac.2024021. [Online]. Available at: http://dx.doi.org/10.3934/cac.2024021
  14. M. Heinkenschloss and N. J. Kroeger, “A new diagonalization based method for parallel-in-time solution of linear-quadratic optimal control problems,” ESAIM: Control, Optimisation and Calculus of Variations, vol. 30, p. 62, 2024, doi: 10.1051/cocv/2024051. [Online]. Available at: http://dx.doi.org/10.1051/cocv/2024051
  15. B. Heinzelreiter and J. W. Pearson, “Diagonalization-Based Parallel-in-Time Preconditioners for Instationary Fluid Flow Control Problems,” arXiv:2405.18964v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2405.18964v1
  16. S. Y. Hon, P. Y. Fung, and X.-lei Lin, “An optimal parallel-in-time preconditioner for parabolic optimal control problems,” arXiv:2410.22686v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2410.22686v1
  17. J. Hope-Collins, A. Hamdan, W. Bauer, L. Mitchell, and C. Cotter, “asQ: parallel-in-time finite element simulations using ParaDiag for geoscientific models and beyond,” arXiv:2409.18792v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2409.18792v1
  18. J. Huang, L. Ju, and Y. Xu, “A parareal exponential integrator finite element method for semilinear parabolic equations,” Numerical Methods for Partial Differential Equations, May 2024, doi: 10.1002/num.23116. [Online]. Available at: http://dx.doi.org/10.1002/num.23116
  19. Y.-Y. Huang, P. Y. Fung, S. Y. Hon, and X.-L. Lin, “An efficient preconditioner for evolutionary partial differential equations with θ-method in time discretization,” arXiv:2408.03535v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2408.03535v1
  20. Y.-Y. Huang, S. Y. Hon, L.-K. Chou, and S.-L. Lei, “Optimal preconditioners for nonsymmetric multilevel Toeplitz systems with application to solving non-local evolutionary partial differential equations,” arXiv:2409.15770v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2409.15770v1
  21. C. Iacob, H. Abdulsamad, and S. Särkkä, “A Parallel-in-Time Newton’s Method for Nonlinear Model Predictive Control,” arXiv:2409.20027v2 [math.OC], 2024 [Online]. Available at: http://arxiv.org/abs/2409.20027v2
  22. A. Q. Ibrahim, S. Götschel, and D. Ruprecht, “Space-time parallel scaling of Parareal with a Fourier Neural Operator as coarse propagator,” arXiv:2404.02521v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2404.02521v1
  23. S. Iqbal, H. Abdulsamad, T. Cator, U. Braga-Neto, and S. Särkkä, “Parallel-in-Time Probabilistic Solutions for Time-Dependent Nonlinear Partial Differential Equations,” in 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), 2024, pp. 1–6, doi: 10.1109/mlsp58920.2024.10734739 [Online]. Available at: http://dx.doi.org/10.1109/MLSP58920.2024.10734739
  24. J. Jackaman and S. MacLachlan, “Space-time waveform relaxation multigrid for Navier-Stokes,” arXiv:2407.13997v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2407.13997v1
  25. N. Janssens and J. Meyers, “Parallel-in-time multiple shooting for optimal control problems governed by the Navier–Stokes equations,” Computer Physics Communications, vol. 296, p. 109019, Mar. 2024, doi: 10.1016/j.cpc.2023.109019. [Online]. Available at: http://dx.doi.org/10.1016/j.cpc.2023.109019
  26. F. C. Joseph and G. Gurrala, “Adaptive Homotopy Based Coarse Solver for Parareal-in-Time Transient Stability Simulations,” IEEE Transactions on Power Systems, pp. 1–12, 2024, doi: 10.1109/tpwrs.2024.3424555. [Online]. Available at: http://dx.doi.org/10.1109/TPWRS.2024.3424555
  27. L. Kaiser, R. Tsai, and C. Klingenberg, “Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model,” arXiv:2402.02304v4 [math.AP], 2024 [Online]. Available at: http://arxiv.org/abs/2402.02304v4
  28. A. Kumar, “Investigation of Second Order Taylor Series in the Coarse Operator of Parareal Algorithm for Power System Simulation,” IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1–1, 2024, doi: 10.1109/tcsii.2024.3381372. [Online]. Available at: http://dx.doi.org/10.1109/TCSII.2024.3381372
  29. F. Kwok and D. N. Tognon, “A parallel in time algorithm based ParaExp for optimal control problems,” arXiv:2406.11478v1 [cs.DC], 2024 [Online]. Available at: http://arxiv.org/abs/2406.11478v1
  30. F. Li and Y. Xu, “A Diagonalization-Based Parallel-in-Time Algorithm for Crank-Nicolson’s Discretization of the Viscoelastic Equation,” East Asian Journal on Applied Mathematics, vol. 14, no. 1, pp. 47–78, Jun. 2024, doi: 10.4208/eajam.2022-304.070323. [Online]. Available at: http://dx.doi.org/10.4208/eajam.2022-304.070323
  31. K.-A. Mardal, J. Sogn, and S. Takacs, “A robust and time-parallel preconditioner for parabolic reconstruction problems using Isogeometric Analysis,” arXiv:2407.17964v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2407.17964v1
  32. N. Margenberg and P. Munch, “A Space-Time Multigrid Method for Space-Time Finite Element Discretizations of Parabolic and Hyperbolic PDEs,” arXiv:2408.04372v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2408.04372v1
  33. Z. Miao, B. W. null, and Y. Jiang, “Energy-Preserving Parareal-RKN Algorithms for Hamiltonian Systems,” Numerical Mathematics: Theory, Methods and Applications, vol. 17, no. 1, pp. 121–144, Jun. 2024, doi: 10.4208/nmtma.oa-2023-0081. [Online]. Available at: http://dx.doi.org/10.4208/nmtma.oa-2023-0081
  34. Z. Miao, R.-H. Zhang, W.-W. Han, and Y.-L. Jiang, “Analysis of a fractional-step parareal algorithm for the incompressible Navier-Stokes equations,” Computers & Mathematics with Applications, vol. 161, pp. 78–89, May 2024, doi: 10.1016/j.camwa.2024.02.035. [Online]. Available at: http://dx.doi.org/10.1016/j.camwa.2024.02.035
  35. S. Muralikrishnan and R. Speck, “ParaPIF: A Parareal Approach for Parallel-in-Time Integration of Particle-in-Fourier schemes,” arXiv:2407.00485v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2407.00485v1
  36. S. J. P. Pamela et al., “Neural-Parareal: Dynamically Training Neural Operators as Coarse Solvers for Time-Parallelisation of Fusion MHD Simulations,” arXiv:2405.01355v1 [physics.plasm-ph], 2024 [Online]. Available at: http://arxiv.org/abs/2405.01355v1
  37. B. Park, “Stochastic Power System Dynamic Simulation Using Parallel-in-Time Algorithm,” IEEE Access, vol. 12, pp. 28500–28510, 2024, doi: 10.1109/access.2024.3367358. [Online]. Available at: http://dx.doi.org/10.1109/ACCESS.2024.3367358
  38. N. A. Petersson, S. Günther, and S. W. Chung, “A time-parallel multiple-shooting method for large-scale quantum optimal control,” arXiv:2407.13950v1 [quant-ph], 2024 [Online]. Available at: http://arxiv.org/abs/2407.13950v1
  39. Y. Poirier, J. Salomon, A. Babarit, P. Ferrant, and G. Ducrozet, “Acceleration of a wave-structure interaction solver by the Parareal method,” Engineering Analysis with Boundary Elements, vol. 167, p. 105870, Oct. 2024, doi: 10.1016/j.enganabound.2024.105870. [Online]. Available at: http://dx.doi.org/10.1016/j.enganabound.2024.105870
  40. J. Sarpe, A. Klaedtke, and H. D. Gersem, “Periodic Adjoint Sensitivity Analysis,” arXiv:2405.19048v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2405.19048v1
  41. E. Scheiber, “A Convergence Theorem for the Parareal Algorithm Revisited,” arXiv:2405.06954v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2405.06954v1
  42. E. Schnaubelt, M. Wozniak, J. Dular, I. C. Garcia, A. Verweij, and S. Schöps, “Parallel-in-Time Integration of Transient Phenomena in No-Insulation Superconducting Coils Using Parareal,” arXiv:2404.13333v1 [cs.CE], 2024 [Online]. Available at: http://arxiv.org/abs/2404.13333v1
  43. G. R. de Souza, S. Pezzuto, and R. Krause, “High-order parallel-in-time method for the monodomain equation in cardiac electrophysiology,” arXiv:2405.19994v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2405.19994v1
  44. H. D. Sterck, R. D. Falgout, O. A. Krzysik, and J. B. Schroder, “Parallel-in-time solution of scalar nonlinear conservation laws,” arXiv:2401.04936v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2401.04936v1
  45. H. D. Sterck, R. D. Falgout, O. A. Krzysik, and J. B. Schroder, “Parallel-in-time solution of hyperbolic PDE systems via characteristic-variable block preconditioning,” arXiv:2407.03873v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2407.03873v1
  46. S.-L. Wu and T. Zhou, “Convergence Analysis of the Parareal Algorithm with Nonuniform Fine Time Grid,” SIAM Journal on Numerical Analysis, vol. 62, no. 5, pp. 2308–2330, Oct. 2024, doi: 10.1137/23m1592481. [Online]. Available at: http://dx.doi.org/10.1137/23M1592481
  47. N. K. Yamaleev and S. Paudel, “A New Parallel-in-time Direct Inverse Method for Nonlinear Differential Equations,” arXiv:2406.00878v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2406.00878v1
  48. Z. Yang, Y. Wang, and W. T. Leung, “Parallel in time partially explicit splitting scheme for high contrast multiscale problems,” arXiv:2411.09244v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2411.09244v1
  49. R. Yoda, M. Bolten, K. Nakajima, and A. Fujii, “Coarse-grid operator optimization in multigrid reduction in time for time-dependent Stokes and Oseen problems,” Japan Journal of Industrial and Applied Mathematics, Apr. 2024, doi: 10.1007/s13160-024-00652-8. [Online]. Available at: http://dx.doi.org/10.1007/s13160-024-00652-8
  50. L. Zhang and Q. Zhang, “Convergence analysis of the parareal algorithms for stochastic Maxwell equations driven by additive noise,” arXiv:2407.10907v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2407.10907v1
  51. Y.-L. Zhao, X.-M. Gu, and C. W. Oosterlee, “A parallel preconditioner for the all-at-once linear system from evolutionary PDEs with Crank-Nicolson discretization,” arXiv:2401.16113v1 [math.NA], 2024 [Online]. Available at: http://arxiv.org/abs/2401.16113v1
  52. M. Zhen, X. Liu, X. Ding, and J. Cai, “High-order space–time parallel computing of the Navier–Stokes equations,” Computer Methods in Applied Mechanics and Engineering, vol. 423, p. 116880, Apr. 2024, doi: 10.1016/j.cma.2024.116880. [Online]. Available at: http://dx.doi.org/10.1016/j.cma.2024.116880
  53. M. Zhen, X. Ding, K. Qu, J. Cai, and S. Pan, “Enhancing the Convergence of the Multigrid-Reduction-in-Time Method for the Euler and Navier–Stokes Equations,” Journal of Scientific Computing, vol. 100, no. 2, Jun. 2024, doi: 10.1007/s10915-024-02596-0. [Online]. Available at: http://dx.doi.org/10.1007/s10915-024-02596-0
  54. T. Baumann, S. Götschel, T. Lunet, D. Ruprecht, and R. Speck, “Adaptive time step selection for spectral deferred correction,” Numerical Algorithms, Oct. 2024, doi: 10.1007/s11075-024-01964-z. [Online]. Available at: https://doi.org/10.1007/s11075-024-01964-z
  55. T. Baumann, S. Götschel, T. Lunet, D. Ruprecht, and R. Speck, “Resilience Against Soft Faults through Adaptivity in Spectral Deferred Correction.” 2024 [Online]. Available at: https://arxiv.org/abs/2412.00529
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2023

  1. A. Barman and A. Sharma, “A Space-Time framework for compressible flow simulations using Finite Volume Method,” in AIAA AVIATION 2023 Forum, 2023, doi: 10.2514/6.2023-3431 [Online]. Available at: https://doi.org/10.2514/6.2023-3431
  2. M. Bolten, S. Friedhoff, and J. Hahne, “Task graph-based performance analysis of parallel-in-time methods,” Parallel Computing, vol. 118, p. 103050, Nov. 2023, doi: 10.1016/j.parco.2023.103050. [Online]. Available at: https://doi.org/10.1016/j.parco.2023.103050
  3. N. Bosch, A. Corenflos, F. Yaghoobi, F. Tronarp, P. Hennig, and S. Särkkä, “Parallel-in-Time Probabilistic Numerical ODE Solvers,” arXiv:2310.01145v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2310.01145v1
  4. I. Bossuyt, S. Vandewalle, and G. Samaey, “Monte-Carlo/Moments micro-macro Parareal method for unimodal and bimodal scalar McKean-Vlasov SDEs,” arXiv:2310.11365v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2310.11365v1
  5. A. Bouillon, G. Samaey, and K. Meerbergen, “On generalized preconditioners for time-parallel parabolic optimal control,” arXiv:2302.06406v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2302.06406v1
  6. A. Bouillon, G. Samaey, and K. Meerbergen, “Diagonalization-based preconditioners and generalized convergence bounds for ParaOpt,” arXiv:2304.09235v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2304.09235v1
  7. L. D’Amore and R. Cacciapuoti, “Space-Time Decomposition of Kalman Filter,” Numerical Mathematics: Theory, Methods and Applications, vol. 0, no. 0, pp. 0–0, Sep. 2023, doi: 10.4208/nmtma.oa-2022-0203. [Online]. Available at: https://doi.org/10.4208/nmtma.oa-2022-0203
  8. R. Cacciapuoti and L. D’Amore, “Scalability analysis of a two level domain decomposition approach in space and time solving data assimilation models,” Concurrency and Computation: Practice and Experience, Nov. 2023, doi: 10.1002/cpe.7937. [Online]. Available at: https://doi.org/10.1002/cpe.7937
  9. J. G. Caldas Steinstraesser, P. da Silva Peixoto, and M. Schreiber, “Parallel-in-time integration of the shallow water equations on the rotating sphere using Parareal and MGRIT,” arXiv:2306.09497v1 [math.NA], 2023 [Online]. Available at: https://arxiv.org/abs/2306.09497v1
  10. B. Carrel, M. J. Gander, and B. Vandereycken, “Low-rank Parareal: a low-rank parallel-in-time integrator,” BIT Numerical Mathematics, vol. 63, no. 1, Feb. 2023, doi: 10.1007/s10543-023-00953-3. [Online]. Available at: https://doi.org/10.1007%2Fs10543-023-00953-3
  11. Z. Chen and Y. Liu, “Efficient and Parallel Solution of High-order Continuous Time Galerkin for Dissipative and Wave Propagation Problems,” arXiv:2303.05008v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2303.05008v1
  12. T. Cheng, H. Yang, J. Huang, and C. Yang, “Nonlinear parallel-in-time simulations of multiphase flow in porous media,” Journal of Computational Physics, p. 112515, Sep. 2023, doi: 10.1016/j.jcp.2023.112515. [Online]. Available at: https://doi.org/10.1016/j.jcp.2023.112515
  13. E. C. Cyr, “A 2-Level Domain Decomposition Preconditioner for KKT Systems with Heat-Equation Constraints,” arXiv:2305.04421v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2305.04421v1
  14. C. Dajana, C. Eduardo, and V. Carmine, “Non-stationary wave relaxation methods for general linear systems of Volterra equations: convergence and parallel GPU implementation,” Numerical Algorithms, Jun. 2023, doi: 10.1007/s11075-023-01567-0. [Online]. Available at: https://doi.org/10.1007/s11075-023-01567-0
  15. F. Danieli, B. S. Southworth, and J. B. Schroder, “Space-Time Block Preconditioning for Incompressible Resistive Magnetohydrodynamics,” arXiv:2309.00768v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2309.00768v1
  16. Y. A. Erlangga, “Parallel-in-time Multilevel Krylov Methods: A Prototype,” arXiv:2401.00228v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2401.00228v1
  17. L. Fang, S. Vandewalle, and J. Meyers, “An SQP-based multiple shooting algorithm for large-scale PDE-constrained optimal control problems,” Journal of Computational Physics, vol. 477, p. 111927, Mar. 2023, doi: 10.1016/j.jcp.2023.111927. [Online]. Available at: https://doi.org/10.1016/j.jcp.2023.111927
  18. R. Fang and R. Tsai, “Stabilization of parareal algorithms for long time computation of a class of highly oscillatory Hamiltonian flows using data,” arXiv:2309.01225v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2309.01225v1
  19. S. Frei and A. Heinlein, “Towards parallel time-stepping for the numerical simulation of atherosclerotic plaque growth,” Journal of Computational Physics, vol. 491, p. 112347, Oct. 2023, doi: 10.1016/j.jcp.2023.112347. [Online]. Available at: https://doi.org/10.1016%2Fj.jcp.2023.112347
  20. M. J. Gander and D. Palitta, “A new ParaDiag time-parallel time integration method,” arXiv:2304.12597v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2304.12597v1
  21. M. J. Gander, T. Lunet, D. Ruprecht, and R. Speck, “A Unified Analysis Framework for Iterative Parallel-in-Time Algorithms,” SIAM Journal on Scientific Computing, vol. 45, no. 5, pp. A2275–A2303, 2023, doi: 10.1137/22M1487163. [Online]. Available at: https://doi.org/10.1137/22M1487163
  22. P. Gangl, M. Gobrial, and O. Steinbach, “A space-time finite element method for the eddy current approximation of rotating electric machines,” arXiv:2307.00278v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2307.00278v1
  23. G. Garai and B. C. Mandal, “Linear and Nonlinear Parareal Methods for the Cahn-Hilliard Equation,” arXiv:2304.14074v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2304.14074v1
  24. G. Garai and B. C. Mandal, “Diagonalization based Parallel-in-Time method for a class of fourth order time dependent PDEs,” Mathematics and Computers in Simulation, Aug. 2023, doi: 10.1016/j.matcom.2023.07.028. [Online]. Available at: https://doi.org/10.1016%2Fj.matcom.2023.07.028
  25. J. Hahne, B. Polenz, I. Kulchytska-Ruchka, S. Friedhoff, S. Ulbrich, and S. Schöps, “Parallel-in-time optimization of induction motors,” Journal of Mathematics in Industry, vol. 13, no. 1, Jun. 2023, doi: 10.1186/s13362-023-00134-5. [Online]. Available at: https://doi.org/10.1186/s13362-023-00134-5
  26. S. Hon and S. Serra-Capizzano, “A block Toeplitz preconditioner for all-at-once systems from linear wave equations,” ETNA - Electronic Transactions on Numerical Analysis, vol. 58, pp. 177–195, 2023, doi: 10.1553/etna_vol58s177. [Online]. Available at: https://doi.org/10.1553/etna_vol58s177
  27. S. Hon, J. Dong, and S. Serra-Capizzano, “A preconditioned MINRES method for optimal control of wave equations and its asymptotic spectral distribution theory,” arXiv:2307.12850v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2307.12850v1
  28. A. Q. Ibrahim, S. Götschel, and D. Ruprecht, “Parareal with a Physics-Informed Neural Network as Coarse Propagator,” in Euro-Par 2023: Parallel Processing, Springer Nature Switzerland, 2023, pp. 649–663 [Online]. Available at: https://doi.org/10.1007/978-3-031-39698-4_44
  29. Y. Jiang and J. Liu, “Fast parallel-in-time quasi-boundary value methods for backward heat conduction problems,” Applied Numerical Mathematics, vol. 184, pp. 325–339, Feb. 2023, doi: 10.1016/j.apnum.2022.10.006. [Online]. Available at: https://doi.org/10.1016%2Fj.apnum.2022.10.006
  30. Y. Jiang, J. Liu, and X.-S. Wang, “A direct parallel-in-time quasi-boundary value method for inverse space-dependent source problems,” Journal of Computational and Applied Mathematics, vol. 423, p. 114958, May 2023, doi: 10.1016/j.cam.2022.114958. [Online]. Available at: https://doi.org/10.1016%2Fj.cam.2022.114958
  31. B. Jin, Q. Lin, and Z. Zhou, “Learning Coarse Propagators in Parareal Algorithm,” arXiv:2311.15320v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2311.15320v1
  32. R. Kraft, S. Koraltan, M. Gattringer, F. Bruckner, D. Suess, and C. Abert, “Parallel-in-Time Integration of the Landau-Lifshitz-Gilbert Equation with the Parallel Full Approximation Scheme in Space and Time,” arXiv:2310.11819v1 [physics.comp-ph], 2023 [Online]. Available at: http://arxiv.org/abs/2310.11819v1
  33. S. Leveque, L. Bergamaschi, Á. Martínez, and J. W. Pearson, “Fast Iterative Solver for the All-at-Once Runge–Kutta Discretization,” arXiv:2303.02090v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2303.02090v1
  34. G. Li, “Wavelet-based Edge Multiscale Parareal Algorithm for subdiffusion equations with heterogeneous coefficients in a large time domain,” arXiv:2307.06529v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2307.06529v1
  35. J. Li and Y. Jiang, “Analysis of a New Accelerated Waveform Relaxation Method Based on the Time-Parallel Algorithm,” Journal of Scientific Computing, vol. 96, no. 3, Jul. 2023, doi: 10.1007/s10915-023-02285-4. [Online]. Available at: https://doi.org/10.1007/s10915-023-02285-4
  36. X.-lei Lin and S. Hon, “A block α-circulant based preconditioned MINRES method for wave equations,” arXiv:2306.03574v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2306.03574v1
  37. Z. Miao and Y.-L. Jiang, “A Fast Simulation Approach to Switched Systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1–1, 2023, doi: 10.1109/tcsii.2023.3332694. [Online]. Available at: http://dx.doi.org/10.1109/TCSII.2023.3332694
  38. P. Munch, I. Dravins, M. Kronbichler, and M. Neytcheva, “Stage-Parallel Fully Implicit Runge–Kutta Implementations with Optimal Multilevel Preconditioners at the Scaling Limit,” SIAM Journal on Scientific Computing, pp. S71–S96, Jul. 2023, doi: 10.1137/22m1503270. [Online]. Available at: https://doi.org/10.1137%2F22m1503270
  39. V.-T. Nguyen and L. Grigori, “Interpretation of parareal as a two-level additive Schwarz in time preconditioner and its acceleration with GMRES,” Numerical Algorithms, Mar. 2023, doi: 10.1007/s11075-022-01492-8. [Online]. Available at: https://doi.org/10.1007/s11075-022-01492-8
  40. H. Nguyen and R. Tsai, “Numerical wave propagation aided by deep learning,” Journal of Computational Physics, vol. 475, p. 111828, Feb. 2023, doi: 10.1016/j.jcp.2022.111828. [Online]. Available at: https://doi.org/10.1016%2Fj.jcp.2022.111828
  41. B. Philippi and T. Slawig, “A Micro-Macro Parareal Implementation for the Ocean-Circulation Model FESOM2,” arXiv:2306.17269v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2306.17269v1
  42. B. Philippi, M. S. Miraz, and T. Slawig, “A Micor-Macro parallel-in-time Implementation for the 2D Navier-Stokes Equations,” arXiv:2309.03037v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2309.03037v1
  43. J. Sarpe, A. Klaedtke, and H. D. Gersem, “A Parallel-In-Time Adjoint Sensitivity Analysis for a B6 Bridge-Motor Supply Circuit,” arXiv:2307.00802v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2307.00802v1
  44. J. Schleuß, K. Smetana, and L. ter Maat, “Randomized Quasi-Optimal Local Approximation Spaces in Time,” SIAM Journal on Scientific Computing, vol. 45, no. 3, pp. A1066–A1096, May 2023, doi: 10.1137/22m1481002. [Online]. Available at: https://doi.org/10.1137%2F22m1481002
  45. X. Shan and M. B. van Gijzen, “Parareal Method for Anisotropic Diffusion Denoising,” in Parallel Processing and Applied Mathematics, Springer International Publishing, 2023, pp. 313–322 [Online]. Available at: https://doi.org/10.1007/978-3-031-30445-3_26
  46. B. Song, J.-Y. Wang, and Y.-L. Jiang, “Analysis of a New Krylov subspace enhanced parareal algorithm for time-periodic problems,” Numerical Algorithms, Nov. 2023, doi: 10.1007/s11075-023-01704-9. [Online]. Available at: http://dx.doi.org/10.1007/s11075-023-01704-9
  47. Y. Takahashi, K. Fujiwara, and T. Iwashita, “Parallel-in-Space-and-Time Finite-Element Method for Time-Periodic Magnetic Field Problems with Hysteresis,” IEEE Transactions on Magnetics, pp. 1–1, 2023, doi: 10.1109/tmag.2023.3307498. [Online]. Available at: https://doi.org/10.1109/tmag.2023.3307498
  48. K. Trotti, “A domain splitting strategy for solving PDEs,” arXiv:2303.01163v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2303.01163v1
  49. D. A. Vargas, R. D. Falgout, S. Günther, and J. B. Schroder, “Multigrid Reduction in Time for Chaotic Dynamical Systems,” SIAM Journal on Scientific Computing, vol. 45, no. 4, pp. A2019–A2042, Aug. 2023, doi: 10.1137/22m1518335. [Online]. Available at: https://doi.org/10.1137%2F22m1518335
  50. Y. Wang, “Parallel Numerical Picard Iteration Methods,” Journal of Scientific Computing, vol. 95, no. 1, Mar. 2023, doi: 10.1007/s10915-023-02156-y. [Online]. Available at: https://doi.org/10.1007/s10915-023-02156-y
  51. M. Wang and S. Zhang, “A Preconditioner for Galerkin–Legendre Spectral All-at-Once System from Time-Space Fractional Diffusion Equation,” Symmetry, vol. 15, no. 12, p. 2144, Dec. 2023, doi: 10.3390/sym15122144. [Online]. Available at: http://dx.doi.org/10.3390/sym15122144
  52. S.-L. Wu, Z. Wang, and T. Zhou, “PinT Preconditioner for Forward-Backward Evolutionary Equations,” SIAM Journal on Matrix Analysis and Applications, vol. 44, no. 4, pp. 1771–1798, Nov. 2023, doi: 10.1137/22m1516476. [Online]. Available at: http://dx.doi.org/10.1137/22M1516476
  53. H. Yamazaki, C. J. Cotter, and B. A. Wingate, “Time-parallel integration and phase averaging for the nonlinear shallow-water equations on the sphere,” Quarterly Journal of the Royal Meteorological Society, Jul. 2023, doi: 10.1002/qj.4517. [Online]. Available at: https://doi.org/10.1002%2Fqj.4517
  54. X. Yue, Z. Wang, and S.-L. Wu, “Convergence Analysis of a Mixed Precision Parareal Algorithm,” SIAM Journal on Scientific Computing, vol. 45, no. 5, pp. A2483–A2510, Sep. 2023, doi: 10.1137/22m1510169. [Online]. Available at: https://doi.org/10.1137/22m1510169
  55. J. Zeifang, A. T. Manikantan, and J. Schütz, “Time parallelism and Newton-adaptivity of the two-derivative deferred correction discontinuous Galerkin method,” Applied Mathematics and Computation, vol. 457, p. 128198, Nov. 2023, doi: 10.1016/j.amc.2023.128198. [Online]. Available at: https://doi.org/10.1016/j.amc.2023.128198
  56. Q. Zhou, Y. Liu, and S.-L. Wu, “Parareal algorithm via Chebyshev-Gauss spectral collocation method,” arXiv:2304.10152v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2304.10152v1
  57. Z. Zhou, H. Gu, G. Ju, and W. Xing, “A Parallel-in-time Method Based on Preconditioner for Biot’s Model,” arXiv:2310.10430v1 [math.NA], 2023 [Online]. Available at: http://arxiv.org/abs/2310.10430v1
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2022

  1. W. C. Agboh, D. Ruprecht, and M. R. Dogar, “Combining Coarse and Fine Physics for Manipulation using Parallel-in-Time Integration,” in Robotics Research, 2022, pp. 725–740, doi: 10.1007/978-3-030-95459-8_44 [Online]. Available at: https://doi.org/10.1007/978-3-030-95459-8_44
  2. A. Arrarás, F. J. Gaspar, L. Portero, and C. Rodrigo, “Space-Time Parallel Methods for Evolutionary Reaction-Diffusion Problems,” in Domain Decomposition Methods in Science and Engineering XXVI, Springer International Publishing, 2022, pp. 643–651 [Online]. Available at: https://doi.org/10.1007/978-3-030-95025-5_70
  3. D. Q. Bui, C. Japhet, Y. Maday, and P. Omnes, “Coupling Parareal with Optimized Schwarz Waveform Relaxation for Parabolic Problems,” SIAM Journal on Numerical Analysis, vol. 60, no. 3, pp. 913–939, May 2022, doi: 10.1137/21m1419428. [Online]. Available at: https://doi.org/10.1137/21m1419428
  4. T. Cheng, N. Lin, and V. Dinavahi, “Hybrid Parallel-in-Time-and-Space Transient Stability Simulation of Large-Scale AC/DC Grids,” IEEE Transactions on Power Systems, pp. 1–1, 2022, doi: 10.1109/tpwrs.2022.3153450. [Online]. Available at: https://doi.org/10.1109/tpwrs.2022.3153450
  5. S. Costanzo, T. Sayadi, M. F. de Pando, P. J. Schmid, and P. Frey, “Parallel-in-time adjoint-based optimization – application to unsteady incompressible flows,” Journal of Computational Physics, p. 111664, Oct. 2022, doi: 10.1016/j.jcp.2022.111664. [Online]. Available at: https://doi.org/10.1016/j.jcp.2022.111664
  6. L. D’Amore, E. Constantinescu, and L. Carracciuolo, “A Scalable Space-Time Domain Decomposition Approach for Solving Large Scale Nonlinear Regularized Inverse Ill Posed Problems in 4D Variational Data Assimilation,” Journal of Scientific Computing, vol. 91, no. 2, Apr. 2022, doi: 10.1007/s10915-022-01826-7. [Online]. Available at: https://doi.org/10.1007/s10915-022-01826-7
  7. F. Danieli, B. S. Southworth, and A. J. Wathen, “Space-Time Block Preconditioning for Incompressible Flow,” SIAM Journal on Scientific Computing, vol. 44, no. 1, pp. A337–A363, Feb. 2022, doi: 10.1137/21m1390773. [Online]. Available at: https://doi.org/10.1137%2F21m1390773
  8. F. Danieli and S. MacLachlan, “Multigrid reduction in time for non-linear hyperbolic equations,” ETNA - Electronic Transactions on Numerical Analysis, vol. 58, pp. 43–65, 2022, doi: 10.1553/etna_vol58s43. [Online]. Available at: https://doi.org/10.1553%2Fetna_vol58s43
  9. S. Frei and A. Heinlein, “Efficient coarse correction for parallel time-stepping in plaque growth simulations,” arXiv:2207.02081v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2207.02081v1
  10. I. C. Garcia, I. Kulchytska-Ruchka, and S. Schöps, “Parareal for index two differential algebraic equations,” Numerical Algorithms, Mar. 2022, doi: 10.1007/s11075-022-01267-1. [Online]. Available at: https://doi.org/10.1007%2Fs11075-022-01267-1
  11. O. Gorynina, F. Legoll, T. Lelievre, and D. Perez, “Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects,” arXiv:2212.10508v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2212.10508v1
  12. J. Hahne, B. S. Southworth, and S. Friedhoff, “Asynchronous Truncated Multigrid-Reduction-in-Time,” SIAM Journal on Scientific Computing, pp. S281–S306, Nov. 2022, doi: 10.1137/21m1433149. [Online]. Available at: https://doi.org/10.1137/21m1433149
  13. G. He, “Time Parallel Denoising Algorithm Based on P-M Equation for Real Image,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–9, Aug. 2022, doi: 10.1155/2022/8008912. [Online]. Available at: https://doi.org/10.1155/2022/8008912
  14. Y. He and J. Liu, “A Vanka-type multigrid solver for complex-shifted Laplacian systems from diagonalization-based parallel-in-time algorithms,” Applied Mathematics Letters, vol. 132, p. 108125, Oct. 2022, doi: 10.1016/j.aml.2022.108125. [Online]. Available at: https://doi.org/10.1016/j.aml.2022.108125
  15. K. Herb and P. Welter, “Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines,” Computer Physics Communications, vol. 270, p. 108181, Jan. 2022, doi: 10.1016/j.cpc.2021.108181. [Online]. Available at: https://doi.org/10.1016/j.cpc.2021.108181
  16. Y. Jiang and J. Liu, “Fast Parallel-in-Time Quasi-Boundary Value Methods for Backward Heat Conduction Problems,” Applied Numerical Mathematics, Oct. 2022, doi: 10.1016/j.apnum.2022.10.006. [Online]. Available at: https://doi.org/10.1016/j.apnum.2022.10.006
  17. E. Kazakov, D. Efremenko, V. Zemlyakov, and J. Gao, “A Time-Parallel Ordinary Differential Equation Solver with an Adaptive Step Size: Performance Assessment,” in Lecture Notes in Computer Science, Springer International Publishing, 2022, pp. 3–17 [Online]. Available at: https://doi.org/10.1007/978-3-031-22941-1_1
  18. D. Kressner, S. Massei, and J. Zhu, “Improved parallel-in-time integration via low-rank updates and interpolation,” arXiv:2204.03073v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2204.03073v1
  19. Y. Lee, J. Park, and C.-O. Lee, “Parareal Neural Networks Emulating a Parallel-in-Time Algorithm,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2022, doi: 10.1109/tnnls.2022.3206797. [Online]. Available at: https://doi.org/10.1109/tnnls.2022.3206797
  20. C.-O. Lee, Y. Lee, and J. Park, “A Parareal Architecture for Very Deep Convolutional Neural Networks,” in Lecture Notes in Computational Science and Engineering, Springer International Publishing, 2022, pp. 407–415 [Online]. Available at: http://dx.doi.org/10.1007/978-3-030-95025-5_43
  21. F. Legoll, T. Lelièvre, and U. Sharma, “An Adaptive Parareal Algorithm: Application to the Simulation of Molecular Dynamics Trajectories,” SIAM Journal on Scientific Computing, vol. 44, no. 1, pp. B146–B176, Jan. 2022, doi: 10.1137/21m1412979. [Online]. Available at: https://doi.org/10.1137/21m1412979
  22. S. Li, L. Xie, and L. Zhou, “Convergence analysis of space-time domain decomposition method for parabolic equations,” Computers &\mathsemicolon Mathematics with Applications, Aug. 2022, doi: 10.1016/j.camwa.2022.08.012. [Online]. Available at: https://doi.org/10.1016/j.camwa.2022.08.012
  23. J. Liu and Z. Wang, “A ROM-accelerated parallel-in-time preconditioner for solving all-at-once systems in unsteady convection-diffusion PDEs,” vol. 416, p. 126750, Mar. 2022, doi: 10.1016/j.amc.2021.126750. [Online]. Available at: https://doi.org/10.1016/j.amc.2021.126750
  24. J. Liu, X.-S. Wang, S.-L. Wu, and T. Zhou, “A well-conditioned direct PinT algorithm for first- and second-order evolutionary equations,” Advances in Computational Mathematics, vol. 48, no. 3, Apr. 2022, doi: 10.1007/s10444-022-09928-4. [Online]. Available at: https://doi.org/10.1007%2Fs10444-022-09928-4
  25. C. Lohmann, J. Dünnebacke, and S. Turek, “Fourier analysis of a time-simultaneous two-grid algorithm using a damped Jacobi waveform relaxation smoother for the one-dimensional heat equation,” Journal of Numerical Mathematics, vol. 0, no. 0, Jun. 2022, doi: 10.1515/jnma-2021-0045. [Online]. Available at: https://doi.org/10.1515/jnma-2021-0045
  26. G. E. Moon and E. C. Cyr, “Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences,” arXiv:2203.04738v1 [cs.CV], 2022 [Online]. Available at: http://arxiv.org/abs/2203.04738v1
  27. K. Pentland, M. Tamborrino, D. Samaddar, and L. C. Appel, “Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization,” SIAM Journal on Scientific Computing, pp. S82–S102, Jul. 2022, doi: 10.1137/21m1414231. [Online]. Available at: https://doi.org/10.1137%2F21m1414231
  28. K. Pentland, M. Tamborrino, and T. J. Sullivan, “Error bound analysis of the stochastic parareal algorithm,” arXiv:2211.05496v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2211.05496v1
  29. K. Pentland, M. Tamborrino, T. J. Sullivan, J. Buchanan, and L. C. Appel, “GParareal: a time-parallel ODE solver using Gaussian process emulation,” Statistics and Computing, vol. 33, no. 1, Dec. 2022, doi: 10.1007/s11222-022-10195-y. [Online]. Available at: https://doi.org/10.1007%2Fs11222-022-10195-y
  30. B. Philippi and T. Slawig, “The Parareal Algorithm Applied to the FESOM 2 Ocean Circulation Model,” arXiv:2208.07598v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2208.07598v1
  31. M. K. Riahi, “PiTSBiCG: Parallel in time Stable Bi-Conjugate gradient algorithm,” Applied Numerical Mathematics, vol. 181, pp. 225–233, Nov. 2022, doi: 10.1016/j.apnum.2022.06.004. [Online]. Available at: https://doi.org/10.1016%2Fj.apnum.2022.06.004
  32. S. Riffo, F. Kwok, and J. Salomon, “Time-parallelization of sequential data assimilation problems,” arXiv:2212.02377v1 [math.OC], 2022 [Online]. Available at: http://arxiv.org/abs/2212.02377v1
  33. J. Rosemeier, T. Haut, and B. Wingate, “Multi-level Parareal algorithm with Averaging,” arXiv:2211.17239v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2211.17239v1
  34. S. Särkkä and Á. F. García-Fernández, “Temporal Parallelisation of the HJB Equation and Continuous-Time Linear Quadratic Control,” arXiv:2212.11744v1 [math.OC], 2022 [Online]. Available at: http://arxiv.org/abs/2212.11744v1
  35. H. D. Sterck, R. D. Falgout, and O. A. Krzysik, “Fast multigrid reduction-in-time for advection via modified semi-Lagrangian coarse-grid operators,” arXiv:2203.13382v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2203.13382v1
  36. H. D. Sterck, S. Friedhoff, O. A. Krzysik, and S. P. MacLachlan, “Multigrid reduction-in-time convergence for advection problems: A Fourier analysis perspective,” arXiv:2208.01526v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2208.01526v1
  37. H. D. Sterck, R. D. Falgout, O. A. Krzysik, and J. B. Schroder, “Efficient multigrid reduction-in-time for method-of-lines discretizations of linear advection,” arXiv:2209.06916v1 [math.NA], 2022 [Online]. Available at: http://arxiv.org/abs/2209.06916v1
  38. J. Strake, D. Döhring, and A. Benigni, “MGRIT-Based Multi-Level Parallel-in-Time Electromagnetic Transient Simulation,” Energies, vol. 15, no. 21, p. 7874, Oct. 2022, doi: 10.3390/en15217874. [Online]. Available at: https://doi.org/10.3390/en15217874
  39. M. Sugiyama, J. B. Schroder, B. S. Southworth, and S. Friedhoff, “Weighted relaxation for multigrid reduction in time,” Numerical Linear Algebra with Applications, Sep. 2022, doi: 10.1002/nla.2465. [Online]. Available at: https://doi.org/10.1002%2Fnla.2465
  40. M. A. Sultanov, V. E. Misilov, and Y. Nurlanuly, “Efficient Parareal algorithm for solving time-fractional diffusion equation,” Dal nevostochnyi Matematicheskii Zhurnal, vol. 22, no. 2, pp. 245–251, 2022, doi: 10.47910/femj202233. [Online]. Available at: https://doi.org/10.47910/femj202233
  41. Y. Takahashi, K. Fujiwara, and T. Iwashita, “Parallel-in-space-and-time finite-element analysis of electric machines using time step overlapping in a massively parallel computing environment,” COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Jul. 2022, doi: 10.1108/compel-04-2022-0161. [Online]. Available at: https://doi.org/10.1108/compel-04-2022-0161
  42. R. Tielen, M. Möller, and C. Vuik, “Combining p-multigrid and Multigrid Reduction in Time methods to obtain a scalable solver for Isogeometric Analysis,” SN Applied Sciences, vol. 4, no. 6, May 2022, doi: 10.1007/s42452-022-05043-7. [Online]. Available at: https://doi.org/10.1007%2Fs42452-022-05043-7
  43. Utkarsh, C. Elrod, Y. Ma, K. Althaus, and C. Rackauckas, “Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations,” in 2022 IEEE High Performance Extreme Computing Conference (HPEC), 2022, doi: 10.1109/hpec55821.2022.9926357 [Online]. Available at: https://doi.org/10.1109%2Fhpec55821.2022.9926357
  44. C.-Y. Wang, Y.-L. Jiang, and Z. Miao, “Time domain decomposition of parabolic control problems based on discontinuous Galerkin semi-discretization,” Applied Numerical Mathematics, Feb. 2022, doi: 10.1016/j.apnum.2022.02.016. [Online]. Available at: https://doi.org/10.1016/j.apnum.2022.02.016
  45. R. Watschinger, M. Merta, G. Of, and J. Zapletal, “A Parallel Fast Multipole Method for a Space-Time Boundary Element Method for the Heat Equation,” SIAM Journal on Scientific Computing, vol. 44, no. 4, pp. C320–C345, Aug. 2022, doi: 10.1137/21m1430157. [Online]. Available at: https://doi.org/10.1137%2F21m1430157
  46. J. Yang, Z. Yuan, and Z. Zhou, “Robust Convergence of Parareal Algorithms with Arbitrarily High-Order Fine Propagators,” SSRN Electronic Journal, 2022, doi: 10.2139/ssrn.4097528. [Online]. Available at: https://doi.org/10.2139%2Fssrn.4097528
  47. L. Yang and H. Li, “A hybrid algorithm based on parareal and Schwarz waveform relaxation,” Electronic Research Archive, vol. 30, no. 11, pp. 4086–4107, 2022, doi: 10.3934/era.2022207. [Online]. Available at: https://doi.org/10.3934/era.2022207
  48. R. Yoda, M. Bolten, K. Nakajima, and A. Fujii, “Assignment of idle processors to spatial redistributed domains on coarse levels in multigrid reduction in time,” in International Conference on High Performance Computing in Asia-Pacific Region, 2022, doi: 10.1145/3492805.3492810 [Online]. Available at: https://doi.org/10.1145/3492805.3492810
  49. R. Yoda, M. Bolten, K. Nakajima, and A. Fujii, “Acceleration of Optimized Coarse-Grid Operators by Spatial Redistribution for Multigrid Reduction in Time,” in Computational Science – ICCS 2022, Springer International Publishing, 2022, pp. 214–221 [Online]. Available at: https://doi.org/10.1007/978-3-031-08754-7_29
  50. R.-H. Zhang, Y.-L. Jiang, J. Li, and B. Song, “Analysis of the parareal algorithm for linear parametric differential equations,” International Journal of Computer Mathematics, pp. 1–0, Nov. 2022, doi: 10.1080/00207160.2022.2153225. [Online]. Available at: https://doi.org/10.1080/00207160.2022.2153225
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2021

  1. J. Angel, S. Götschel, and D. Ruprecht, “Impact of spatial coarsening on Parareal convergence,” arXiv:2111.10228v1 [math.NA], 2021 [Online]. Available at: http://arxiv.org/abs/2111.10228v1
  2. P. Benedusi, M. L. Minion, and R. Krause, “An experimental comparison of a space-time multigrid method with PFASST for a reaction-diffusion problem,” Computers & Mathematics with Applications, vol. 99, pp. 162–170, Oct. 2021, doi: 10.1016/j.camwa.2021.07.008. [Online]. Available at: https://doi.org/10.1016%2Fj.camwa.2021.07.008
  3. S. Blanes, “Novel parallel in time integrators for ODEs,” Applied Mathematics Letters, p. 107542, Jul. 2021, doi: 10.1016/j.aml.2021.107542. [Online]. Available at: https://doi.org/10.1016/j.aml.2021.107542
  4. A. L. Blumers, M. Yin, H. Nakajima, Y. Hasegawa, Z. Li, and G. E. Karniadakis, “Multiscale parareal algorithm for long-time mesoscopic simulations of microvascular blood flow in zebrafish,” Computational Mechanics, Aug. 2021, doi: 10.1007/s00466-021-02062-w. [Online]. Available at: https://doi.org/10.1007%2Fs00466-021-02062-w
  5. T. Buvoli and M. Minion, “IMEX Runge-Kutta Parareal for Non-diffusive Equations,” in Springer Proceedings in Mathematics &\mathsemicolon Statistics, Springer International Publishing, 2021, pp. 95–127 [Online]. Available at: https://doi.org/10.1007%2F978-3-030-75933-9_5
  6. M. Cai, J. Mahseredjian, I. Kocar, X. Fu, and A. Haddadi, “A parallelization-in-time approach for accelerating EMT simulations,” Electric Power Systems Research, vol. 197, p. 107346, Aug. 2021, doi: 10.1016/j.epsr.2021.107346. [Online]. Available at: https://doi.org/10.1016/j.epsr.2021.107346
  7. J. G. Caldas Steinstraesser, “Coupling large and small scale shallow water models with porosity in the presence of anisotropy,” PhD thesis, Université de Montpellier, 2021 [Online]. Available at: https://www.theses.fr/2021MONTS040
  8. J. G. Caldas Steinstraesser, V. Guinot, and A. Rousseau, “Modified parareal method for solving the two-dimensional nonlinear shallow water equations using finite volumes,” The SMAI journal of computational mathematics, vol. 7, pp. 159–184, 2021, doi: 10.5802/smai-jcm.75. [Online]. Available at: https://smai-jcm.centre-mersenne.org/articles/10.5802/smai-jcm.75/
  9. M. Caliari, L. Einkemmer, A. Moriggl, and A. Ostermann, “An accurate and time-parallel rational exponential integrator for hyperbolic and oscillatory PDEs,” Journal of Computational Physics, vol. 437, p. 110289, Jul. 2021, doi: 10.1016/j.jcp.2021.110289. [Online]. Available at: https://doi.org/10.1016%2Fj.jcp.2021.110289
  10. J. Chaudhry, D. Estep, and S. Tavener, “A posteriori error analysis for a space-time parallel discretization of parabolic partial differential equations,” arXiv:2111.00606v1 [math.NA], 2021 [Online]. Available at: http://arxiv.org/abs/2111.00606v1
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top

2020

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top

2019

  1. A. L. Blumers, Z. Li, and G. E. Karniadakis, “Supervised parallel-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to hydrodynamics,” Journal of Computational Physics, vol. 393, pp. 214–228, 2019, doi: 10.1016/j.jcp.2019.05.016. [Online]. Available at: https://doi.org/10.1016/j.jcp.2019.05.016
  2. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-Driven Time Parallelism via Forecasting,” SIAM Journal on Scientific Computing, vol. 41, no. 3, pp. B466–B496, Jan. 2019, doi: 10.1137/18m1174362. [Online]. Available at: https://doi.org/10.1137/18m1174362
  3. S. Dohr, J. Zapletal, G. Of, M. Merta, and M. Kravčenko, “A parallel space–time boundary element method for the heat equation,” Computers & Mathematics with Applications, 2019, doi: https://doi.org/10.1016/j.camwa.2018.12.031. [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0898122118307296
  4. S. Friedhoff, J. Hahne, I. Kulchytska-Ruchka, and S. Schöps, “Exploring Parallel-in-Time Approaches for Eddy Current Problems,” in Progress in Industrial Mathematics at ECMI 2018, Springer International Publishing, 2019, pp. 373–379 [Online]. Available at: https://doi.org/10.1007/978-3-030-27550-1_47
  5. S. Friedhoff, J. Hahne, and S. Schöps, “Multigrid-reduction-in-time for Eddy Current problems,” PAMM, vol. 19, no. 1, Nov. 2019, doi: 10.1002/pamm.201900262. [Online]. Available at: https://doi.org/10.1002/pamm.201900262
  6. S. Friedhoff and B. S. Southworth, “On ‘Optimal’ h-Independent Convergence of Parareal and MGRIT Using Runge-Kutta Time Integration,” arXiv:1906.06672 [math.NA], 2019 [Online]. Available at: https://arxiv.org/abs/1906.06672
  7. M. Gander, L. Halpern, J. Rannou, and J. Ryan, “A Direct Time Parallel Solver by Diagonalization for the Wave Equation,” SIAM Journal on Scientific Computing, vol. 41, no. 1, pp. A220–A245, 2019, doi: 10.1137/17M1148347. [Online]. Available at: https://doi.org/10.1137/17M1148347
  8. M. Gander, Y. Jiang, and B. Song, “A Superlinear Convergence Estimate for the Parareal Schwarz Waveform Relaxation Algorithm,” SIAM Journal on Scientific Computing, vol. 41, no. 2, pp. A1148–A1169, 2019, doi: 10.1137/18M1177226. [Online]. Available at: https://doi.org/10.1137/18M1177226
  9. M. J. Gander, I. Kulchytska-Ruchka, I. Niyonzima, and S. Schöps, “A New Parareal Algorithm for Problems with Discontinuous Sources,” SIAM Journal on Scientific Computing, vol. 41, no. 2, pp. B375–B395, 2019, doi: 10.1137/18M1175653. [Online]. Available at: https://doi.org/10.1137/18M1175653
  10. M. J. Gander and S.-L. Wu, “Convergence analysis of a periodic-like waveform relaxation method for initial-value problems via the diagonalization technique,” Numerische Mathematik, vol. 143, no. 2, pp. 489–527, Jun. 2019, doi: 10.1007/s00211-019-01060-8. [Online]. Available at: https://doi.org/10.1007/s00211-019-01060-8
  11. S. Götschel and M. L. Minion, “An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEs,” SIAM Journal on Scientific Computing, vol. 41, no. 6, pp. C603–C626, Jan. 2019, doi: 10.1137/19m1239313. [Online]. Available at: https://doi.org/10.1137/19m1239313
  12. F. Hédin and T. Lelièvre, “gen.parRep: A first implementation of the Generalized Parallel Replica dynamics for the long time simulation of metastable biochemical systems,” Computer Physics Communications, 2019, doi: 10.1016/j.cpc.2019.01.005. [Online]. Available at: https://doi.org/10.1016/j.cpc.2019.01.005
  13. J. Hong, X. Wang, and L. Zhang, “Parareal Exponential \textdollar}theta\textdollar-Scheme for Longtime Simulation of Stochastic Schrödinger Equations with Weak Damping,” SIAM Journal on Scientific Computing, vol. 41, no. 6, pp. B1155–B1177, Jan. 2019, doi: 10.1137/18m1176749. [Online]. Available at: https://doi.org/10.1137/18m1176749
  14. A. Howse, H. Sterck, R. Falgout, S. MacLachlan, and J. Schroder, “Parallel-In-Time Multigrid with Adaptive Spatial Coarsening for The Linear Advection and Inviscid Burgers Equations,” SIAM Journal on Scientific Computing, vol. 41, no. 1, pp. A538–A565, 2019, doi: 10.1137/17M1144982. [Online]. Available at: https://dx.doi.org/10.1137/17M1144982
  15. O. A. Krzysik, H. D. Sterck, S. P. MacLachlan, and S. Friedhoff, “On selecting coarse-grid operators for Parareal and MGRIT applied to linear advection,” arXiv:1902.07757 [math.NA], 2019 [Online]. Available at: https://arxiv.org/abs/1902.07757
  16. F. Kwok and B. Ong, “Schwarz Waveform Relaxation with Adaptive Pipelining,” SIAM Journal on Scientific Computing, vol. 41, no. 1, pp. A339–A364, 2019, doi: 10.1137/17M115311X. [Online]. Available at: https://doi.org/10.1137/17M115311X
  17. S. Li, R. Chen, and X. Shao, “Parallel two-level space–time hybrid Schwarz method for solving linear parabolic equations,” Applied Numerical Mathematics, vol. 139, pp. 120–135, 2019, doi: 10.1016/j.apnum.2019.01.016. [Online]. Available at: https://doi.org/10.1016/j.apnum.2019.01.016
  18. S. Li, X. Shao, and X.-C. Cai, “Highly parallel space-time domain decomposition methods for parabolic problems,” CCF Transactions on High Performance Computing, 2019, doi: 10.1007/s42514-019-00003-x. [Online]. Available at: https://doi.org/10.1007/s42514-019-00003-x
  19. V. Mele, D. Romano, E. M. Constantinescu, L. Carracciuolo, and L. D’Amore, “Performance Evaluation for a PETSc Parallel-in-Time Solver Based on the MGRIT Algorithm,” in Euro-Par 2018: Parallel Processing Workshops, 2019, pp. 716–728, doi: 10.1002/cpe.4928 [Online]. Available at: https://doi.org/10.1002/cpe.4928
  20. M. Neumüller and I. Smears, “Time-Parallel Iterative Solvers for Parabolic Evolution Equations,” SIAM Journal on Scientific Computing, vol. 41, no. 1, pp. C28–C51, 2019, doi: 10.1137/18M1172466. [Online]. Available at: https://doi.org/10.1137/18M1172466
  21. A. G. Peddle, T. Haut, and B. Wingate, “Parareal Convergence for Oscillatory PDEłowercases with Finite Time-Scale Separation,” SIAM Journal on Scientific Computing, vol. 41, no. 6, pp. A3476–A3497, Jan. 2019, doi: 10.1137/17m1131611. [Online]. Available at: https://doi.org/10.1137/17m1131611
  22. Rosa-Raı́ces Jorge L., B. Zhang, and T. F. Miller, “Path-accelerated stochastic molecular dynamics: Parallel-in-time integration using path integrals,” The Journal of Chemical Physics, vol. 151, no. 16, p. 164120, Oct. 2019, doi: 10.1063/1.5125455. [Online]. Available at: https://doi.org/10.1063/1.5125455
  23. D. Samaddar, D. P. Coster, X. Bonnin, L. A. Berry, W. R. Elwasif, and D. B. Batchelor, “Application of the parareal algorithm to simulations of ELMs in ITER plasma,” Computer Physics Communications, vol. 235, pp. 246–257, 2019, doi: 10.1016/j.cpc.2018.08.007. [Online]. Available at: https://doi.org/10.1016/j.cpc.2018.08.007
  24. M. Schreiber, N. Schaeffer, and R. Loft, “Exponential Integrators with Parallel-in-Time Rational Approximations for Shallow-Water Equations on the Rotating Sphere,” Parallel Computing, 2019, doi: 10.1016/j.parco.2019.01.005. [Online]. Available at: https://dx.doi.org/10.1016/j.parco.2019.01.005
  25. M. Schreiber and R. Loft, “A parallel time integrator for solving the linearized shallow water equations on the rotating sphere,” Numerical Linear Algebra with Applications, vol. 26, no. 2, p. e2220, 2019, doi: 10.1002/nla.2220. [Online]. Available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/nla.2220
  26. B. S. Southworth, “Necessary Conditions and Tight Two-level Convergence Bounds for Parareal and Multigrid Reduction in Time,” SIAM J. Matrix Anal. Appl., vol. 40, no. 2, pp. 564–608, 2019, doi: https://doi.org/10.1137/18M1226208.