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 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 