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 | 117 | 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 | 98 | 2010 |
Solving incompressible two-phase flows on multi-GPU clusters P Zaspel, M Griebel Computers & Fluids 80, 356-364, 2013 | 89 | 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 | 25 | 2014 |
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 | 13 | 2023 |
Algorithmic Patterns for -Matrices on Many-Core Processors P Zaspel Journal of Scientific Computing 78 (2), 1174-1206, 2019 | 13 | 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 |
Optimized multifidelity machine learning for quantum chemistry V Vinod, U Kleinekathöfer, P Zaspel Machine Learning: Science and Technology 5 (1), 015054, 2024 | 8 | 2024 |
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 |
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 |
QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules V Vinod, P Zaspel Scientific Data 12 (1), 202, 2025 | 3 | 2025 |
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 | 3 | 2019 |
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 |
Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties V Vinod, P Zaspel Machine Learning: Science and Technology 5 (4), 045005, 2024 | 2 | 2024 |
Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes P Zaspel, M Günther arXiv preprint arXiv:2406.18726, 2024 | 2 | 2024 |
TOWARD DATA-DRIVEN FILTERS IN PARAVIEW D Maharjan, P Zaspel Journal of Flow Visualization and Image Processing 29 (3), 2022 | 2 | 2022 |
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 |