Publications

159.

K. Shaju, T. Laepple and P. Zaspel, "Bayesian Inference: Kernel-Based Model for Surface Temperature Reconstruction in Ice Borehole Thermometry", arXiv preprint, 2026.

DOI: 10.48550/arXiv.2607.20322

158.

K. Lund and M. Schweitzer, "On the stability of basis construction in extended Krylov subspace methods" , 2026.

DOI: 10.48550/arXiv.2610.21603

157.

V. Vinod and P. Zaspel, "LFaB: Low Fidelity as Bias for Active Learning in the Chemical Configuration Space", J. Chem. Theory Comput., vol. 22, no. 11, pp. 5637-5648, 2026.

DOI: 10.1021/acs.jctc.6c00009

156.

V. Vinod and P. Zaspel, "Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning", 2026.

DOI: 10.48550/arXiv.2606.02662

155.

L. Schaub and P. Zaspel, "Variational Free Energy Pivot Selection for Pivoted Cholesky", arXiv preprint, 2026.

DOI: 10.48550/arXiv.2606.01821

154.

S. Maity, V. Vinod, P. Zaspel and U. Kleinekathöfer, "∆-Machine Learning for LC-DFT-level Excitation Energies of Bacteriochlorophyll Molecules in a LH2 Complex", ChemRxiv preprint, 2026.

DOI: 10.26434/chemrxiv.15002714/v1

153.

X. Liu and M. Schweitzer, "Field-of-values analysis of augmented Krylov methods for matrix $\varphi$-function actions" , 2026.

DOI: 10.48550/arXiv.2607.17819

152.

E. Krieger and M. Schweitzer, "A general framework for Krylov ODE residuals with applications to randomized Krylov methods", Electron. Trans. Numer. Anal., vol. 65, pp. 414-440, 2026.

DOI: 10.1553/etna_vol65s414

151.

D. Lyu, V. Vinod, M. Holzenkamp, 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.", Adv. Theory Simul., vol. 8, no. 11, pp. e00271, 2025.

DOI: 10.1002/adts.202500271

150.

V. Vinod and P. Zaspel, "Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies", J. Chem. Theory Comput., vol. 21, no. 6, pp. 3077-3091, 2025.

DOI: 10.1021/acs.jctc.4c01491

149.

V. Vinod and P. Zaspel, "QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules", Sci. Data, vol. 12, no. 1, pp. 202, 2025. Nature Publishing Group.

DOI: 10.1038/s41597-024-04247-3

148.

M. Hamadi, N. Mahdavi-Amiri and M. Schweitzer, "Splitting methods based on the nonzero diagonal pattern for computing matrix functions" , 2025.

DOI: 10.48550/arXiv.2408.04128

147.

M. Holzenkamp, D. Lyu, U. Kleinekathöfer and P. Zaspel, "Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.", Machine Learning: Science and Technology, 2025.

DOI: 10.1088/2632-2153/ae09ef

146.

V. Vinod and P. Zaspel, "Benchmarking data efficiency in Δ-ML and multifidelity models for quantum chemistry", The Journal of Chemical Physics, vol. 163, no. 2, pp. 024134, 2025.

DOI: 10.1063/5.0272457

145.

M. Schweitzer, "Near instance optimality of the Lanczos method for Stieltjes and related matrix functions", SIAM J. Matrix Anal. Appl., vol. 46, pp. 1846-1865, 2025.

DOI: 10.1137/25M1739650

144.

V. Vinod, D. Lyu, M. Ruth, P. R. Schreiner, U. Kleinekathöfer and P. Zaspel, "Predicting Molecular Energies of Small Organic Molecules With Multi-Fidelity Methods", J. Comp. Chem., vol. 46, no. 6, pp. e70056, 2025.

DOI: 10.1002/jcc.70056

143.

K. Shaju, T. Laepple, N. Hirsch and P. Zaspel, "Ice borehole thermometry: Sensor placement using greedy optimal sampling", Geoscientific Instrumentation, Methods and Data Systems, vol. 14, no. 2, pp. 459—474, 2025.

DOI: 10.5194/gi-14-459-2025

142.

D. Palitta, M. Schweitzer and V. Simoncini, "Sketched and truncated polynomial Krylov methods: Evaluation of matrix functions", Numer. Linear Algebra Appl., vol. 32, pp. e2596, 2025.

DOI: 10.1002/nla.2596

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., vol. 94, pp. 801-823, 2025.

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

140.

V. Patel, D. A. Maldonado, M. Melnichenko, N. Pritchard, V. Rao, E. Rebrova, S. Sankararaman and M. Schweitzer, "Scientific Applications Leveraging Randomized Linear Algebra" , 2025.

DOI: 10.48550/arXiv.2506.16457

139.

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

138.

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

137.

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

136.

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

DOI: 10.48550/arXiv.2401.16132

135.

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

DOI: 10.48550/arXiv.2406.18726

134.

M. Bolten, O. T. Doganay, H. Gottschalk and K. Klamroth, "Non-convex shape optimization by dissipative Hamiltonian flows", Eng. Optim., pp. 1—20, 2024.

DOI: 10.1080/0305215X.2024.2304135

133.

M. Bolten, M. E. Kilmer and S. MacLachlan, "Multigrid preconditioning for regularized least-squares problems", SIAM J. Sci. Comput., vol. 46, no. 5, pp. s271—s295, 2024.

DOI: 10.1137/23M1583417

132.

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", Jpn. J. Ind. Appl. Math., 2024.

DOI: 10.1007/s13160-024-00652-8

131.

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

130.

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

129.

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

128.

V. Vinod and P. Zaspel,QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules [dataset], Oct. 2024.

127.

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

126.

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

125.

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

124.

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

123.

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

122.

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

121.

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

120.

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

119.

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

118.

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

117.

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

DOI: 10.1007/s10092-023-00527-3

116.

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

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.

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

113.

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

112.

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

111.

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

110.

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

109.

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

DOI: 10.1615/JFlowVisImageProc.2022040189

108.

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

107.

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

106.

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

105.

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

104.

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

103.

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

102.

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

101.

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

100.

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

99.

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

98.

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

97.

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

96.

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

95.

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

94.

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

93.

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

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.

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

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.

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

88.

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

87.

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

86.

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

85.

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

84.

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

83.

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

82.

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

81.

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

80.

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

79.

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

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.

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

76.

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

75.

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

74.

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

73.

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

DOI: 10.1615/Int.J.UncertaintyQuantification.2018024865

72.

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

71.

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

70.

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

69.

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

68.

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

67.

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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66.

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

65.

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

64.

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

63.

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

62.

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

61.

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

60.

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

59.

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

58.

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

DOI: 10.5451/unibas-ep69936

57.

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

56.

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

55.

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

54.

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

53.

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

52.

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

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.

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

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.

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

47.

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

46.

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

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.

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

43.

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

42.

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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41.

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

40.

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

39.

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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38.

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

37.

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

36.

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

35.

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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34.

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

33.

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

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.

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.

29.

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

28.

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

27.

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

26.

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

25.

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

24.

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

23.

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

22.

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

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.

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

19.

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

18.

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

DOI: 10.1002/pamm.201210311

17.

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

16.

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

15.

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

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.

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.

12.

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

11.

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

10.

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

9.

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

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.

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

DOI: 10.1002/pamm.201010314

6.

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, Dec. 2010.

DOI: 10.1002/pamm.201010020

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

DOI: 10.1002/pamm.200700223

3.

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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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