26.

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.

25.

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

24.

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

23.

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.

22.

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

21.

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.

20.

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.

19.

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

18.

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

17.

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.

16.

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

15.

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.

14.

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.

13.

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.

12.

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.

11.

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

10.

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

9.

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

8.

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

7.

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.

6.

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

5.

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.

4.

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

3.

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.

2.

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.

1.

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.