Veröffentlichungen

141.

A. Frommer, M. Rinelli and M. Schweitzer, "Analysis of stochastic probing methods for estimating the trace of functions of sparse symmetric matrices", Math. Comp., 2024.

DOI: https://doi.org/10.1090/mcom/3984

140.

V. Vinod, U. Kleinekathöfer and P. Zaspel, "Optimized multifidelity machine learning for quantum chemistry", Mach. Learn.: Sci. Technol., vol. 5, no. 1, pp. 015054, 2024.

DOI: 10.1088/2632-2153/ad2cef

139.

V. Vinod and P. Zaspel, "Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties", Machine Learning: Science and Technology, vol. 5, no. 4, pp. 045005, 2024.

DOI: 10.1088/2632-2153/ad7f25

138.

D. Palitta, M. Schweitzer and V. Simoncini, "Sketched and truncated polynomial Krylov subspace methods: Matrix Sylvester equations", Math. Comp., 2024.

DOI: https://doi.org/10.1090/mcom/4002

137.

V. Vinod and P. Zaspel, "QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules", 2024.

DOI: 10.48550/arXiv.2406.14149

136.

V. Vinod, D. Lyu, M. Ruth, U. Kleinekathöfer, P. R. Schreiner and P. Zaspel, "Predicting Molecular Energies of Small Organic Molecules with Multifidelity Methods.", 2024.

DOI: 10.26434/chemrxiv-2024-9zz16

135.

A. Frommer, G. Ramirez-Hidalgo, M. Schweitzer and M. Tsolakis, "Polynomial preconditioning for the action of the matrix square root and inverse square root", Electron. Trans. Numer. Anal., vol. 60, pp. 381-404, 2024.

DOI: 10.1553/etna_vol60s381

134.

B. Arslan, S. D. Relton and M. Schweitzer, "Structured level-2 condition numbers of matrix functions", Electron. J. Linear Algebra, vol. 40, pp. 28-47, 2024.

DOI: 10.13001/ela.2024.7905

133.

M. A. Botchev, L. A. Knizhnerman and M. Schweitzer, "Krylov subspace residual and restarting for certain second order differential equations", SIAM J. Sci. Comput., vol. 46, no. 2, pp. S223-S253, 2024.

DOI: 10.1137/22M1503300

132.

D. Lyu, M. Holzenkamp, V. Vinod, Y. M. Holtkamp, S. Maity, C. R. Salazar, U. Kleinekathöfer and P. Zaspel, "Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning.", 2024.

DOI: 10.48550/arXiv.2410.20551

131.

M. Holzenkamp, D. Lyu, U. Kleinekathöfer and P. Zaspel, "Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.", 2024.

DOI: 10.48550/arXiv.2410.20398

130.

P. Zaspel and M. Günther, "Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes.", 2024.

DOI: 10.48550/arXiv.2406.18726

129.

M. Fasi, S. Gaudreault, K. Lund and M. Schweitzer, "Challenges in computing matrix functions" , 2024.

DOI: 10.48550/arXiv.2401.16132

128.

V. Vinod and P. Zaspel, "Benchmarking Data Efficiency in Δ-ML and Multifidelity Models for Quantum Chemistry.", 2024.

DOI: 10.48550/arXiv.2410.11391

127.

V. Vinod and P. Zaspel, "Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies", 2024.

DOI: 10.48550/arXiv.2410.11392

126.

D. Palitta, M. Schweitzer and V. Simoncini, "Sketched and truncated polynomial Krylov methods: Evaluation of matrix functions" , 2023.

DOI: 10.48550/arXiv.2306.06481

125.

K. Lund and M. Schweitzer, "The Frechet derivative of the tensor t-function", Calcolo, vol. 60, 2023.

DOI: 10.1007/s10092-023-00527-3

124.

M. Bolten, S. Friedhoff and J. Hahne, "Task graph-based performance analysis of parallel-in-time methods", Parallel Comput., vol. 118, pp. 103050, 2023.

DOI: 10.1016/j.parco.2023.103050

123.

M. Bolten, M. Donatelli, P. Ferrari and I. Furci, "Symbol based convergence analysis in multigrid methods for saddle point problems", Linear Algebra Appl., vol. 671, pp. 67--108, 2023.

DOI: 10.1016/j.laa.2023.04.016

122.

M. Bolten, M. Donatelli, P. Ferrari and I. Furci, "Symbol based convergence analysis in block multigrid methods with applications for Stokes problems", Appl. Numer. Math., vol. 193, pp. 109-130, 2023.

DOI: 10.1016/j.apnum.2023.07.016

121.

V. Vinod, S. Maity, P. Zaspel and U. Kleinekathöfer, "Multifidelity Machine Learning for Molecular Excitation Energies", J. Chem. Theory Comput., vol. 19, no. 21, pp. 7658-7670, 2023.

DOI: 10.1021/acs.jctc.3c00882

120.

M. Schweitzer, "Sensitivity of matrix function based network communicability measures: Computational methods and a priori bounds", SIAM J. Matrix Anal. Appl., vol. 44, no. 3, pp. 1321-1348, 2023.

DOI: 10.1137/23M1556708

119.

S. Güttel and M. Schweitzer, "Randomized sketching for Krylov approximations of large-scale matrix functions", SIAM J. Matrix Anal. Appl., vol. 44, pp. 1073-1095, 2023.

DOI: 10.1137/22M1518062

118.

A. Frommer, K. Kahl, M. Schweitzer and M. Tsolakis, "Krylov subspace restarting for matrix Laplace transforms", SIAM J. Matrix Anal. Appl., vol. 44, no. 2, pp. 693-717, 2023.

DOI: 10.1137/22M1499674

117.

M. Schweitzer, "Integral representations for higher-order Frechet derivatives of matrix functions: Quadrature algorithms and new results on the level-2 condition number", Linear Algebra Appl., vol. 656, pp. 247-276, 2023.

DOI: 10.1016/j.laa.2022.10.005

116.

M. Bolten, S. -. Ekström, I. Furci and S. Serra-Capizzano, "A note on the spectral analysis of matrix sequences via GLT momentary symbols: from all-at-once solution of parabolic problems to distributed fractional order matrices", Electron. Trans. Numer. Anal., vol. 58, pp. 136--163, 2023.

DOI: 10.1553/etna_vol58s136

115.

M. Bolten, E. D. Sturler and C. Hahn, "Krylov Subspace Recycling for Evolving Structures", Comput. Methods Appl. Mech. Engrg., vol. 391, pp. 114222, 2022.

DOI: 10.1016/j.cma.2021.114222

114.

M. Bolten, S. -. Ekström, I. Furci and S. Serra-Capizzano, "Toeplitz Momentary Symbols: definition, results, and limitations in the spectral analysis of structured matrices", Linear Algebra Appl., vol. 651, pp. 51-82, 2022.

DOI: 10.1016/j.laa.2022.06.017

113.

D. Maharjan and P. Zaspel, "Toward data-driven filters in paraview", JFV, vol. 29, no. 3, 2022.

DOI: 10.1615/JFlowVisImageProc.2022040189

112.

M. Schweitzer, "Decay bounds for Bernstein functions of Hermitian matrices with applications to the fractional graph Laplacian", Electron. Trans. Numer. Anal., vol. 55, pp. 438-454, 2022.

DOI: 10.1553/etna_vol55s438

111.

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, Groen, Derek and de Mulatier, Clelia and Paszynski, Maciej and Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and Sloot, Peter M. A., Eds. Cham: Springer International Publishing, 2022, pp. 214-221.

DOI: 10.1007/978-3-031-08754-7_29

110.

M. Bolten, M. Donatelli, P. Ferrari and I. Furci, "A symbol-based analysis for multigrid methods for block-circulant and block-Toeplitz systems", SIAM J. Matrix Anal. Appl., vol. 43, no. 1, pp. 405-438, 2022.

DOI: 10.1137/21M1390554

109.

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 Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, 2022, pp. 41-51.

DOI: 10.1145/3492805.3492810

108.

B. Beckermann, A. Cortinovis, D. Kressner and M. Schweitzer, "Low-rank updates of matrix functions II: Rational Krylov methods", SIAM J. Numer. Anal., vol. 59, no. 3, pp. 1325-1347, 2021.

DOI: 10.1137/20M1362553

107.

M. Sugiyama, J. B. Schroder, B. S. Southworth and S. Friedhoff,Weighted Relaxation for Multigrid Reduction in Time, 2021.

106.

M. Bolten, O. T. Doganay, H. Gottschalk and K. Klamroth, "Tracing locally Pareto optimal points by numerical integration", SIAM J. Control Optim., vol. 59, no. 5, pp. 3302-3328, 2021.

DOI: 10.1137/20M1341106

105.

N. Haussmann, M. Zang, R. Mease, M. Clemens, B. Schmuelling and M. Bolten, "Towards real-time magnetic dosimetry simulations for inductive charging systems", COMPEL, 2021.

DOI: 10.1108/COMPEL-03-2021-0084

104.

I. Kulchytska-Ruchka, S. Schöps, M. Hinze, S. Friedhoff and S. Ulbrich, "PASIROM: parallel simulation and robust optimization of electro-mechanical energy converters" in German success stories in industrial mathematics, Springer, Cham, 2021, pp. 135-140.

DOI: 10.1007/978-3-030-81455-7\_22

103.

H. De Sterck, R. D. Falgout, S. Friedhoff, O. A. Krzysik and S. P. MacLachlan, "Optimizing multigrid reduction-in-time and parareal coarse-grid operators for linear advection", Numer. Linear Algebra Appl., vol. 28, no. 4, pp. Paper No. e2367, 22, 2021.

DOI: 10.1002/nla.2367

102.

S. Friedhoff and B. S. Southworth, "On "optimal" $h$-independent convergence of parareal and multigrid-reduction-in-time using Runge-Kutta time integration", Numer. Linear Algebra Appl., vol. 28, no. 3, pp. Paper No. e2301, 30, 2021.

DOI: 10.1002/nla.2301

101.

P. Ferrari, I. Furci and S. Serra-Capizzano, "Multilevel symmetrized Toeplitz structures and spectral distribution results for the related matrix sequences", Electron. J. Linear Algebra, vol. 37, pp. 370-386, 2021.

DOI: 10.13001/ela.2021.5775

100.

M. Donatelli, P. Ferrari, I. Furci, S. Serra-Capizzano and D. Sesana, "Multigrid methods for block-Toeplitz linear systems: convergence analysis and applications", Numer. Linear Algebra Appl., vol. 28, no. 4, pp. Paper No. e2356, 20, 2021.

DOI: 10.1002/nla.2356

99.

L. Claus and M. Bolten, "Non-overlapping block smoothers for the Stokes equations", Num. Lin. Alg. Appl., vol. 28, no. 6, pp. e2389, 2021.

DOI: 10.1002/nla.2389

98.

J. Backhaus, M. Bolten, O. T. Doganay, M. Ehrhardt, B. Engel, C. Frey, H. Gottschalk, M. Günther, C. Hahn, J. Jäschke, P. Jaksch, K. Klamroth, A. Liefke, D. Luft, L. Mäde, V. Marciniak, M. Reese, J. Schultes and V. Schulz, "GivEn - Shape Optimization for Gas Turbines in Volatile Energy Networks" in Mathematical Modeling, Simulation and Optimization for Power Engineering and Management, S. Göttlich and M. Herty and A. Milde, Eds. Cham: Springer, 2021.

DOI: 10.1007/978-3-030-62732-4_4

97.

J. Stapmanns, J. Hahne, M. Helias, M. Bolten, M. Diesmann and D. Dahmen, "Event-based update of synapses in voltage-based learning rules", Front. Neuroinform., vol. 15, pp. 15, 2021.

DOI: 10.3389/fninf.2021.609147

96.

P. Kandolf, A. Koskela, S. D. Relton and M. Schweitzer, "Computing low-rank approximations of the Frechet derivative of a matrix function using Krylov subspace methods", Numer. Linear Algebra Appl., vol. 28, no. 6, pp. e2401, 31, 2021.

DOI: 10.1002/nla.2401

95.

H. Harbrecht, J. D. Jakeman and P. Zaspel, "Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration", CiCP, vol. 29, no. 4, pp. 1152-1185, 2021.

DOI: 10.4208/cicp.OA-2020-0060

94.

J. Hahne, B. S. Southworth and S. Friedhoff,Asynchronous Truncated Multigrid-reduction-in-time (AT-MGRIT), 2021.

93.

P. Ferrari and S. Serra-Capizzano, "Asymptotic spectra of large matrices coming from the symmetrization of Toeplitz structure functions and applications to preconditioning", Numer. Linear Algebra Appl., vol. 28, no. 1, pp. Paper No. e2332, 16, 2021.

DOI: 10.1002/nla.2332

92.

A. Frommer, C. Schimmel and M. Schweitzer, "Analysis of probing techniques for sparse approximation and trace estimation of decaying matrix functions", SIAM J. Matrix Anal. Appl., vol. 42, no. 3, pp. 1290-1318, 2021.

DOI: 10.1137/20M1364461

91.

J. Hahne, S. Friedhoff and M. Bolten, "Algorithm 1016: PyMGRIT: a Python package for the parallel-in-time method MGRIT", ACM Trans. Math. Software, vol. 47, no. 2, pp. Art. 19, 22, 2021.

DOI: 10.1145/3446979

90.

S. Güttel and M. Schweitzer, "A comparison of limited-memory Krylov methods for Stieltjes functions of Hermitian matrices", SIAM J. Matrix Anal. Appl., vol. 42, no. 1, pp. 83-107, 2021.

DOI: 10.1137/20M1351072

89.

P. Ferrari, C. Garoni and S. Serra-Capizzano, "Fast parallel solver for the space-time IgA-DG discretization of the diffusion equation", J. Sci. Comput., vol. 89, no. 1, pp. Paper No. 20, 21, 2021.

DOI: 10.1007/s10915-021-01567-z

88.

M. Bolten, S. Friedhoff, J. Hahne and S. Schöps, "Parallel-in-time simulation of an electrical machine using MGRIT", Comput. Vis. Sci., vol. 23, no. 1-4, pp. Paper No. 14, 14, 2020.

DOI: 10.1007/s00791-020-00333-2

87.

F. Durastante and I. Furci, "Spectral analysis of saddle-point matrices from optimization problems with elliptic PDE constraints", Electron. J. Linear Algebra, vol. 36, pp. 773-798, 2020.

DOI: 10.13001/ela.2020.5151

86.

H. De Sterck, S. Friedhoff, A. J. M. Howse and S. P. MacLachlan, "Convergence analysis for parallel-in-time solution of hyperbolic systems", Numer. Linear Algebra Appl., vol. 27, no. 1, pp. e2271, 31, 2020.

DOI: 10.1002/nla.2271

85.

M. Kawai, A. Ida, H. Matsuba, K. Nakajima and M. Bolten, "Multiplicative Schwartz-Type Block Multi-Color Gauss-Seidel Smoother for Algebraic Multigrid Methods" in Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, New York: ACM, 2020, pp. 217-226.

DOI: 10.1145/3368474.3368481

84.

P. Ferrari and S. Serra-Capizzano, "Multigrid for $\mathbb{Q}_{k}$ Finite Element Matrices using a (block) Toeplitz symbol approach", Mathematics, vol. 8, no. 1, pp. Paper No. 5, 2020.

DOI: 10.3390/math8010005

83.

C. Lengauer, S. Apel, M. Bolten, S. Chiba, U. Rüde, J. Teich, A. Größlinger, F. Hannig, H. Köstler, L. Claus, A. Grebhahn, S. Groth, S. Kronawitter, S. Kuckuk, H. Rittich, C. Schmitt and J. Schmitt, "ExaStencils: Advanced Multigrid Solver Generation" in Software for Exascale Computing - SPPEXA 2016-2019, H. J. Bungartz and S. Reiz and B. Uekermann and P. Neumann and W. Nagel, Eds. Cham: Springer, 2020, pp. 405-452.

DOI: 10.1007/978-3-030-47956-5_14

82.

M. Bolten and L. Claus, "Local Fourier Analysis of multigrid methods for the Stokes problem", PAMM, vol. 19, pp. e201900394, 2019.

DOI: 10.1002/pamm.201900394

81.

P. Ferrari, I. Furci, S. Hon, M. A. Mursaleen and S. Serra-Capizzano, "The eigenvalue distribution of special 2-by-2 block matrix-sequences with applications to the case of symmetrized Toeplitz structures", SIAM J. Matrix Anal. Appl., vol. 40, no. 3, pp. 1066-1086, 2019.

DOI: 10.1137/18M1207399

80.

H. Harbrecht and P. Zaspel, "On the Algebraic Construction of Sparse Multilevel Approximations of Elliptic Tensor Product Problems", J. Sci. Comput., vol. 78, no. 2, pp. 1272-1290, 2019.

DOI: 10.1007/s10915-018-0807-6

79.

M. Bolten, H. Gottschalk, C. Hahn and M. Saadi, "Numerical shape optimization to decrease failure probability of ceramic structures", Comput. Vis. Sci., vol. 21, pp. 1-10, 2019.

DOI: 10.1007/s00791-019-00315-z

78.

S. Friedhoff, J. Hahne and S. Schöps, "Multigrid-reduction-in-time for Eddy Current problems", PAMM, vol. 19, no. 1, pp. e201900262, 2019.

DOI: 10.1002/pamm.201900262

77.

M. Bolten and C. Hahn, "Using composite finite elements for shape optimization with a stochastic objective functional" in Progress in Industrial Mathematics at ECMI 2018, I. Farago and F. Izsak and P. L. Simon, Eds. Cham: Springer, 2019, pp. 515-520.

DOI: 10.1007/978-3-030-27550-1_65

76.

M. Griebel, C. Rieger and P. Zaspel, "Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations", IJUQ, vol. 9, no. 5, 2019.

DOI: 10.1615/Int.J.UncertaintyQuantification.2019029228

75.

P. Zaspel, "Ensemble Kalman filters for reliability estimation in perfusion inference", IJUQ, vol. 9, no. 1, 2019.

DOI: 10.1615/Int.J.UncertaintyQuantification.2018024865

74.

P. Zaspel, B. Huang, H. Harbrecht and O. A. Lilienfeld, "Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited", J. Chem. Theory Comput., vol. 15, no. 3, pp. 1546-1559, 2019.

DOI: 10.1021/acs.jctc.8b00832

73.

M. Bolten and J. Hahne, "An SDE-waveform relaxation method with application in distributed neural network simulations", PAMM, vol. 19, pp. e201900373, 2019.

DOI: 10.1002/pamm.201900373

72.

P. Zaspel, "Algorithmic Patterns for H-Matrices on Many-Core Processors", J. Sci. Comput., vol. 78, no. 2, pp. 1174-1206, 2019.

DOI: 10.1007/s10915-018-0809-4

71.

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, Farago, Istvan and Izsak, Ferenc and Simon, Peter L., Eds. Springer International Publishing, 2019, pp. 373-379.

DOI: 10.1007/978-3-030-27550-1_47

70.

M. Bolten and H. Rittich, "Fourier analysis of periodic stencils in multigrid methods", SIAM J. Sci. Comput., vol. 40, no. 3, pp. A1642-A1668, 2018.

DOI: 10.1137/16M1073959

69.

A. Frommer, C. Schimmel and M. Schweitzer, "Non-Toeplitz decay bounds for inverses of Hermitian positive definite tridiagonal matrices", Electron. Trans. Numer. Anal., vol. 48, pp. 362-372, 2018.

DOI: 10.1553/etna_vol48s362

68.

M. Bolten, K. Kahl, D. Kressner, F. Macedo and S. Sokolović, "Multigrid methods combined with low-rank approximation for tensor-structured Markov chains", Electron. Trans. Numer. Anal., vol. 48, pp. 348-361, 2018.

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67.

B. Beckermann, D. Kressner and M. Schweitzer, "Low-rank updates of matrix functions", SIAM J. Matrix Anal. Appl., vol. 39, no. 1, pp. 539-565, 2018.

DOI: 10.1137/17M1140108

66.

M. Dumbser, F. Fambri, I. Furci, M. Mazza, S. Serra-Capizzano and M. Tavelli, "Staggered discontinuous Galerkin methods for the incompressible Navier-Stokes equations: spectral analysis and computational results", Numer. Linear Algebra Appl., vol. 25, no. 5, pp. e2151, 31, 2018.

DOI: 10.1002/nla.2151

65.

S. Ekström, I. Furci and S. Serra-Capizzano, "Exact formulae and matrix-less eigensolvers for block banded symmetric Toeplitz matrices", BIT, vol. 58, no. 4, pp. 937-968, 2018.

DOI: 10.1007/s10543-018-0715-z

64.

A. Frommer, C. Schimmel and M. Schweitzer, "Bounds for the decay of the entries in inverses and Cauchy-Stieltjes functions of certain sparse, normal matrices", Numer. Linear Algebra Appl., vol. 25, no. 4, pp. e2131, 17, 2018.

DOI: 10.1002/nla.2131

63.

M. Bolten, D. Moser and R. Speck, "Asymptotic convergence of the parallel full approximation scheme in space and time for linear problems", Numer. Lin. Alg. Appl., vol. 25, no. 6, pp. e2208, 2018.

DOI: 10.1002/nla.2208

62.

S. Ekström, I. Furci, C. Garoni, C. Manni, S. Serra-Capizzano and H. Speleers, "Are the eigenvalues of the B-spline isogeometric analysis approximation of $-\Delta u=\lambda u$ known in almost closed form?", Numer. Linear Algebra Appl., vol. 25, no. 5, pp. e2198, 34, 2018.

DOI: 10.1002/nla.2198

61.

F. Ahmad, E. S. Al-Aidarous, D. A. Alrehaili, S. Ekström, I. Furci and S. Serra-Capizzano, "Are the eigenvalues of preconditioned banded symmetric Toeplitz matrices known in almost closed form?", Numer. Algorithms, vol. 78, no. 3, pp. 867-893, 2018.

DOI: 10.1007/s11075-017-0404-z

60.

H. Harbrecht and P. Zaspel, "A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters", 2018.

DOI: 10.5451/unibas-ep70080

59.

A. Grebhahn, C. Engwer, M. Bolten and S. Apel, "Variability of stencil computations for porous media applications", CCPE, vol. 29, no. 17, pp. e4119, 2017.

DOI: 10.1002/cpe.4119

58.

V. Heuveline, M. Schick, C. Webster and P. Zaspel, "Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)", DROPS-IDN/v2/document/10.4230/DagRep.6.9.59, 2017.

DOI: 10.4230/DagRep.6.9.59

57.

A. Frommer, K. Lund, M. Schweitzer and D. B. Szyld, "The Radau-Lanczos method for matrix functions", SIAM J. Matrix Anal. Appl., vol. 38, no. 3, pp. 710-732, 2017.

DOI: 10.1137/16M1072565

56.

S. E. Sayed, M. Bolten and D. Pleiter, "Parallel I/O architecture modelling based on file system counters" in High Performance Computing. ISC High Performance 2016, M. Taufer and B. Mohr and J. Kunkel, Eds. Springer, 2017, pp. 627-637.

DOI: 10.1007/978-3-319-46079-6_43

55.

J. Hahne, D. Dahmen, J. Schuecker, A. Frommer, M. Bolten, M. Helias and M. Diesmann, "Integration of continuous-time dynamics in a spiking neural network simulator", Front. Neuroinform., vol. 11, pp. 00034, 2017.

DOI: 10.3389/fninf.2017.00034

54.

R. D. Falgout, S. Friedhoff, T. V. Kolev, S. P. MacLachlan, J. B. Schroder and S. Vandewalle, "Multigrid methods with space-time concurrency", Comput. Vis. Sci., vol. 18, no. 4-5, pp. 123-143, 2017.

DOI: 10.1007/s00791-017-0283-9

53.

P. Zaspel, "Analysis and parallelizationstrategies for Ruge-Stüben AMG on many-core processors", 2017.

DOI: 10.5451/unibas-ep69936

52.

M. Bolten, F. Franchetti, P. H. Kelly, C. Lengauer and M. Mohr, "Algebraic description and automatic generation of multigrid methods in SPIRAL", CCPE, vol. 29, no. 17, pp. e4105, 2017.

DOI: 10.1002/cpe.4105

51.

M. Schweitzer, "A two-sided short-recurrence extended Krylov subspace method for nonsymmetric matrices and its relation to rational moment matching", Numer. Algorithms, vol. 76, no. 1, pp. 1-31, 2017.

DOI: 10.1007/s11075-016-0239-z

50.

M. Bolten, D. Moser and R. Speck, "A multigrid perspective on the parallel full approximation scheme in space and time", Numer. Lin. Alg. Appl., vol. 24, no. 6, pp. e2110, 2017.

DOI: 10.1002/nla.2110

49.

M. Bolten, K. Kahl and S. Sokolović, "Multigrid methods for Tensor structured Markov chains with low rank approximation", SIAM J. Sci. Comput., vol. 38, no. 2, pp. A649-A667, 2016.

DOI: 10.1137/140994447

48.

P. Zaspel, "Subspace correction methods in algebraic multi-level frames", Linear Algebra Appl., vol. 488, pp. 505-521, 2016.

DOI: 10.1016/j.laa.2015.09.026

47.

M. Bolten, T. Huckle and C. Kravvaritis, "Sparse matrix approximations and the convergence of multigrid methods", Linear Algebra Appl., vol. 502, pp. 58-76, 2016.

DOI: 10.1016/j.laa.2015.11.008

46.

M. Schweitzer, "Restarting and error estimation in polynomial and extended Krylov subspace methods for the approximation of matrix functions", Bergische Universität Wuppertal, Fakultät für Mathematik und Naturwissenschaften, 2016.

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45.

S. E. Sayed, M. Bolten, D. Pleiter and W. Frings, "Parallel I/O characterisation based on server- side performance counters" in 2016 1st Joint International Workshop on Parallel Data Storage and data Intensive Scalable Computing Systems (PDSW-DISCS), 2016, pp. 7-12.

DOI: 10.1109/PDSW-DISCS.2016.006

44.

S. E. Sayed, M. Bolten and D. Pleiter, "Using file system counters in modelling parallel I/O architectures", ACM SIGOPS OSR, vol. 50, no. 2, pp. 37-46, 2016.

DOI: 10.1145/3041710.3041716

43.

M. Schweitzer, "Monotone convergence of the extended Krylov subspace method for Laplace-Stieltjes functions of Hermitian positive definite matrices", Linear Algebra Appl., vol. 507, pp. 486-498, 2016.

DOI: 10.1016/j.laa.2016.06.041

42.

M. Schweitzer, "Any finite convergence curve is possible in the initial iterations of restarted FOM", Electron. Trans. Numer. Anal., vol. 45, pp. 133-145, 2016.

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41.

A. Hessenthaler, S. Friedhoff, O. Röhrle and D. A. Nordsletten, "3D Fluid-Structure Interaction Experiment and Benchmark Results", PAMM, vol. 16, no. 1, pp. 451-452, 2016.

DOI: 10.1002/pamm.201610214

40.

M. Bolten and O. Letterer, "Increasing arithmetic intensity in multigrid methods on GPUs using block smoothers" in PPAM 2015, Part I, R. Wyrzykowski and E. Deelman and J. Dongarra and K. Karczewski and J. Kitowski and K. Wiatr, Eds. Springer, 2016, pp. 515-525.

DOI: 10.1007/978-3-319-32149-3_48

39.

A. Frommer and M. Schweitzer, "Error bounds and estimates for Krylov subspace approximations of Stieltjes matrix functions", BIT, vol. 56, no. 3, pp. 865-892, 2016.

DOI: 10.1007/s10543-015-0596-3

38.

J. Hahne, M. Helias, S. Kunkel, J. Igarashi, I. Kitayama, B. Wylie, M. Bolten, A. Frommer and M. Diesmann, "Including Gap Junctions into Distributed Neuronal Network Simulations" in Brain-Inspired Computing. BrainComp 2015, K. Amunts and L. Grandinetti and T. Lippert and N. Petkov, Eds. Springer, 2016, pp. 43-57.

DOI: 10.1007/978-3-319-50862-7_4

37.

M. Bolten, M. Donatelli, T. Huckle and C. Kravvaritis, "Generalized grid transfer operators for multigrid methods applied on Toeplitz matrices", BIT, vol. 55, pp. 341-366, 2015.

DOI: 10.1007/s10543-014-0512-2

36.

M. Bolten, H. Gottschalk and S. Schmitz, "Minimal failure probability for ceramic design via shape control", J. Optim. Theory Appl., vol. 166, pp. 983-1001, 2015.

DOI: 10.1007/s10957-014-0621-8

35.

M. L. Minion, R. Speck, M. Bolten, M. Emmett and D. Ruprecht, "Interweaving PFASST and parallel multigrid", SIAM J. Sci. Comput., vol. 37, no. 5, pp. S244-S263, 2015.

DOI: 10.1137/14097536X

34.

M. Bolten, M. Donatelli and T. Huckle, "Analysis of smoothed aggregation multigrid methods based on Toeplitz matrices", Electron. Trans. Numer. Anal., vol. 44, pp. 25-52, 2015.

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33.

J. Hahne, M. Helias, S. Kunkel, J. Igarashi, M. Bolten, A. Frommer and M. Diesmann, "A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations", Front. Neuroinform., vol. 9, pp. 00022, 2015.

DOI: 10.3389/fninf.2015.00022

32.

R. Speck, D. Ruprecht, M. Emmett, M. Minion, M. Bolten and R. Krause, "A multi-level spectral deferred correction method", BIT, vol. 55, pp. 843-867, 2015.

DOI: 10.1007/s10543-014-0517-x

31.

S. Friedhoff and S. MacLachlan, "A generalized predictive analysis tool for multigrid methods", Numer. Linear Algebra Appl., vol. 22, no. 4, pp. 618-647, 2015.

DOI: 10.1002/nla.1977

30.

A. Frommer, S. Güttel and M. Schweitzer, "Convergence of restarted Krylov subspace methods for Stieltjes functions of matrices", SIAM J. Matrix Anal. Appl., vol. 35, no. 4, pp. 1602-1624, 2014.

DOI: 10.1137/140973463

29.

A. Frommer, S. Güttel and M. Schweitzer, "Efficient and stable Arnoldi restarts for matrix functions based on quadrature", SIAM J. Matrix Anal. Appl., vol. 35, no. 2, pp. 661-683, 2014.

DOI: 10.1137/13093491X

28.

M. Bolten, "Evaluation of a multigrid solver for 3-level Toeplitz and circulant matrices on Blue Gene/Q" in Proceedings of the NIC Symposium 2014, K. Binder and G. MÜnster and M. Kremer, Eds. Jülich, 2014, pp. 345-352.

27.

D. Pflüger, H. Bungartz, M. Griebel, F. Jenko, T. Dannert, M. Heene, C. Kowitz, A. P. Hinojosa and P. Zaspel, "EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond" in Euro-Par 2014: Parallel Processing Workshops, Lopes, Luís and Žilinskas, Julius and Costan, Alexandru and Cascella, Roberto G. and Kecskemeti, Gabor and Jeannot, Emmanuel and Cannataro, Mario and Ricci, Laura and Benkner, Siegfried and Petit, Salvador and Scarano, Vittorio and Gracia, José and Hunold, Sascha and Scott, Stephen L. and Lankes, Stefan and Lengauer, Christian and Carretero, Jesús and Breitbart, Jens and Alexander, Michael, Eds. 2014, pp. 565-576.

DOI: 10.1007/978-3-319-14313-2_48

26.

C. Lengauer, S. Apel, M. Bolten, A. Größlinger, F. Hannig, U. Rüde, J. Teich, A. Grebhahn, S. Kronawitter, S. Kuckuk, H. Rittich and C. Schmitt, "ExaStencils: Advanced stencil-code engineering" in Euro-Par 2014: Parallel Processing Workshops - Euro-Par 2014 International Workshops, Porto, Portugal, August 25-26, 2014, Revised Selected Papers, Part II, Springer, 2014, pp. 553-564.

DOI: 10.1007/978-3-319-14313-2_47

25.

R. D. Falgout, S. Friedhoff, T. V. Kolev, S. MacLachlan and J. B. Schroder, "Parallel time integration with multigrid", PAMM, vol. 14, no. 1, pp. 951-952, 2014.

DOI: 10.1002/pamm.201410456

24.

R. Speck, D. Ruprecht, M. Emmett, M. Bolten and R. Krause, "A space-time parallel solver for the three-dimensional heat equation" in Parallel Computing: Accelerating Computational Science and Engineering (CSE), M. Bader and A. Bode and H.-J. Bungartz and M. Gerndt and G.R. Joubert and F. Peters, Eds. IOS Press, 2014, pp. 263-272.

DOI: 10.3233/978-1-61499-381-0-263

23.

M. Bolten, N. Bozovic and A. Frommer, "Preconditioning of Krylov subspace methods using recycling in Lattice QCD Computations", PAMM, vol. 13, pp. 413-414, 2013.

DOI: 10.1002/pamm.201310202

22.

P. Zaspel and M. Griebel, "Solving incompressible two-phase flows on multi-GPU clusters", Computers & Fluids, vol. 80, pp. 356-364, 2013.

DOI: 10.1016/j.compfluid.2012.01.021

21.

D. Ruprecht, R. Speck, M. Emmett, M. Bolten and R. Krause, "Extreme- scale space-time parallelism" in 2013 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Denver, CO, USA, 2013.

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20.

A. Arnold, M. Bolten, H. Dachsel, F. Fahrenberger, F. Gähler, R. Halver, F. Heber, M. Hofmann, C. Holm, J. Iseringhausen, I. Kabadshow, O. Lenz, M. Pippig, D. Potts and G. Sutmann, "Comparison of Scalable Fast Methods for Long-Range Interactions", Phys. Rev. E, vol. 88, pp. 063308, 2013.

DOI: 10.1103/PhysRevE.88.063308

19.

S. Friedhoff, S. MacLachlan and C. Börgers, "Local Fourier analysis of space-time relaxation and multigrid schemes", SIAM J. Sci. Comput., vol. 35, no. 5, pp. S250-S276, 2013.

DOI: 10.1137/120881361

18.

K. Samiei, B. Peters, M. Bolten and A. Frommer, "Assessment of the potentials of implicit integration methods in discrete element modelling of granular matter", Comput. Chem. Eng., vol. 49, pp. 183-193, 2012.

DOI: 10.1016/j.compchemeng.2012.10.009

17.

A. Thiess, R. Zeller, M. Bolten, P. H. Dederichs and S. Blügel, "Massively parallel density functional calculations for thousands of atoms: KKRnano", Phys. Rev. B, vol. 85, pp. 235103, 2012.

DOI: 10.1103/PhysRevB.85.235103

16.

M. Bolten and K. Kahl, "Using block smoothers in multigrid methods", PAMM, vol. 12, pp. 645-646, 2012.

DOI: 10.1002/pamm.201210311

15.

M. Bolten, "Multigrid methods for long-range interactions" in Fast methods for long-range interactions in complex systems, G. Sutmann and P. Gibbon and Th. Lippert, Eds. Jülich: Forschungszentrum Jülich, 2011, pp. 115-130.

14.

M. Bolten, A. Thiess, I. Yavneh and R. Zeller, "Preconditioning systems arising from the KKR Green function method using block-circulant matrices", Linear Algebra Appl., vol. 436, no. 2, pp. 436-446, Jan. 2011.

DOI: 10.1016/j.laa.2011.05.019

13.

P. Zaspel and M. Griebel, "Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver", Comput. Visual Sci., vol. 14, no. 8, pp. 371-383, 2011.

DOI: 10.1007/s00791-013-0188-1

12.

P. Zaspel and M. Griebel, "Massively Parallel Fluid Simulations on Amazon's HPC Cloud" in 2011 First International Symposium on Network Cloud Computing and Applications, 2011, pp. 73-78.

DOI: 10.1109/NCCA.2011.19

11.

M. Bolten, D. Brinkers, U. Rüde and M. Stürmer, "Implementation of multigrid on QPACE" in 2011 IEEE International Conference on Cluster Computing (CLUSTER), Los Alamitos, CA, USA: IEEE Computer Society, 2011, pp. 371-377.

DOI: 10.1109/CLUSTER.2011.48

10.

M. Bolten, S. Friedhoff, A. Frommer, M. Heming and K. Kahl, "Algebraic multigrid methods for Laplacians of graphs", Linear Algebra Appl., vol. 434, no. 11, pp. 2225-2243, 2011.

DOI: 10.1016/j.laa.2010.11.008

9.

M. Bolten, A. Brandt, J. Brannick, A. Frommer, K. Kahl and I. Livshits, "A bootstrap algebraic multilevel method for Markov chains", SIAM J. Sci. Comput., vol. 33, no. 6, pp. 3425-3446, 2011.

DOI: 10.1137/100791816

8.

M. Griebel and P. Zaspel, "A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations", Comput Sci Res Dev, vol. 25, no. 1, pp. 65-73, 2010.

DOI: 10.1007/s00450-010-0111-7

7.

K. Samiei, B. Peters, M. Bolten and A. Frommer, "An implicit approach to predict the dynamics of granular media", PAMM, vol. 10, no. 1, pp. 55-56, Dez. 2010.

DOI: 10.1002/pamm.201010020

6.

M. Bolten, "Highly scalable multigrid algorithm for particle simulation", PAMM, vol. 10, no. 1, pp. 643-644, Dez. 2010.

DOI: 10.1002/pamm.201010314

5.

G. Sutmann, L. Westphal and M. Bolten, "Particle Based Simulations of Complex Systems with {MP2C}: Hydrodynamics and Electrostatics", AIP Cong. Proc., vol. 1281, pp. 1768-1772, 2010.

DOI: 10.1063/1.3498216

4.

M. Bolten, "Hierarchical grid coarsening for the solution of the Poisson equation in free space", Electron. Trans. Numer. Anal., vol. 29, pp. 70-80, 2008.

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3.

M. Bolten and G. Sutmann, "NFFT-based extension of a particle simulation method using multigrid", PAMM, vol. 7, no. 1, pp. 2140005-2140006, Dez. 2008.

DOI: 10.1002/pamm.200700223

2.

M. Bolten and G. Sutmann, "A highly accurate and optimal method to calculate long range interactions" in Proceedings of the workshop ``From computational biophysics to systems biology'', J. Meinke and O. Zimmermann and S. Mohanty and U.H.E. Hansmann, Eds. Jülich: John von Neumann Institute for Computing, 2006, pp. 189-192.

1.

M. Bolten, N. Papenberg, B. Fischer, P. Adamidis, R. Rabenseifner and H. Berger, "Parallelisierung eines Nichtlinearen Registrierungsalgorithmus zur Verarbeitung sehr großer Volumen-Daten" in Bildverarbeitung für die Medizin 2005, H. Meinzer and H. Handels and A. Horsch and T. Tolxdorff, Eds. Springer, 2005, pp. 360-364.

DOI: 10.1007/3-540-26431-0_74