| Boosting quantum machine learning models with a multilevel combination technique: Pople diagrams revisited P Zaspel, B Huang, H Harbrecht, OA von Lilienfeld Journal of chemical theory and computation 15 (3), 1546-1559, 2018 | 101 | 2018 |
| A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations M Griebel, P Zaspel Computer Science-Research and Development 25, 65-73, 2010 | 96 | 2010 |
| Solving incompressible two-phase flows on multi-GPU clusters P Zaspel, M Griebel Computers & Fluids 80, 356-364, 2013 | 88 | 2013 |
| Massively parallel fluid simulations on Amazon's HPC cloud P Zaspel, M Griebel 2011 First International Symposium on Network Cloud Computing and …, 2011 | 42 | 2011 |
| EXAHD: an exa-scalable two-level sparse grid approach for higher-dimensional problems in plasma physics and beyond D Pflüger, HJ Bungartz, M Griebel, F Jenko, T Dannert, M Heene, ... Euro-Par 2014: Parallel Processing Workshops: Euro-Par 2014 International …, 2014 | 23 | 2014 |
| Algorithmic Patterns for -Matrices on Many-Core Processors P Zaspel Journal of Scientific Computing 78 (2), 1174-1206, 2019 | 12 | 2019 |
| Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver P Zaspel, M Griebel Computing and visualization in science 14, 371-383, 2011 | 10 | 2011 |
| Cholesky-based experimental design for Gaussian process and kernel-based emulation and calibration H Harbrecht, JD Jakeman, P Zaspel Universität Basel 2020 (05), 2020 | 7 | 2020 |
| Solving incompressible two-phase flows on massively parallel multi-GPU clusters P Zaspel, M Griebel Computers and Fluids, Submitted: INS Preprint, 2011 | 7 | 2011 |
| Optimized multifidelity machine learning for quantum chemistry V Vinod, U Kleinekathöfer, P Zaspel Machine Learning: Science and Technology 5 (1), 015054, 2024 | 6 | 2024 |
| Multifidelity machine learning for molecular excitation energies V Vinod, S Maity, P Zaspel, U Kleinekathöfer Journal of Chemical Theory and Computation 19 (21), 7658-7670, 2023 | 6 | 2023 |
| Uncertainty quantification and high performance computing (dagstuhl seminar 16372) V Heuveline, M Schick, C Webster, P Zaspel Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2017 | 4 | 2017 |
| Parallel RBF Kernel-Based Stochastic Collocation for Large-Scale Random PDEs P Zaspel Universitäts-und Landesbibliothek Bonn, 2015 | 4 | 2015 |
| A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters H Harbrecht, P Zaspel arXiv preprint arXiv:1806.11558, 2018 | 3 | 2018 |
| Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration H Helmut, JD Jakeman, P Zaspel Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020 | 2 | 2020 |
| Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations M Griebel, C Rieger, P Zaspel International Journal for Uncertainty Quantification 9 (5), 2019 | 2 | 2019 |
| Analysis and parallelizationstrategies for Ruge-Stüben AMGon many-core processors PE Zaspel Universität Basel 2017 (06), 2017 | 2 | 2017 |
| Subspace correction methods in algebraic multi-level frames P Zaspel Linear Algebra and its Applications 488, 505-521, 2016 | 2 | 2016 |
| Zweiphasige Navier-Stokes Fluidsimulationen in Maya: Konfiguration, Visualisierung und Animation P Zaspel Diplomarbeit, Institut für Numerische Simulation Universität Bonn, 2009 | 2 | 2009 |
| Multi-Fidelity Machine Learning for Excited State Energies of Molecules V Vinod, S Maity, P Zaspel, U Kleinekathöfer arXiv preprint arXiv:2305.11292, 2023 | 1 | 2023 |
