Preprints
K. Shaju, T. Laepple, N. Hirsch, and P. Zaspel: Ice borehole thermometry: Sensor placement using greedy optimal sampling, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3755, 2025.
D. Lyu, M. Holzenkamp, V. Vinod, YM 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. arXiv:2410.20551. https://doi.org/10.48550/arXiv.2410.20551.
M. Holzenkamp, D. Lyu, U. Kleinekathöfer and P. Zaspel, “Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.”, 2024. arXiv:2410.20398. https://doi.org/10.48550/arXiv.2410.20398.
V. Vinod and P. Zaspel. Benchmarking Data Efficiency in Δ-ML and Multifidelity Models for Quantum Chemistry. 2024. arXiv preprint arXiv:2410.11391, https://arxiv.org/abs/2410.11391.
P. Zaspel and M. Günther, “Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes”, 2024. arXiv preprint arxiv:2406.18726, https://doi.org/10.48550/arXiv.2406.18726.
Peer-Reviewed Articles
V. Vinod, and P. Zaspel. QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules. Sci Data 12, 202, 2025. DOI:https://doi.org/10.1038/s41597-024-04247-3; also available as arXiv:2406.14149.
V. Vinod, D. Lyu, M. Ruth, U. Kleinekathöfer, P. R. Schreiner, and P. Zaspel. Predicting molecular energies of small organic molecules with multifidelity methods. J. Comput. Chem., 46: e70056, 2025. DOI: https://doi.org/10.1002/jcc.70056; also available as chemrxiv-2024-9zz16.
V. Vinod and P. Zaspel. Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies. J. Chem. Theory Comput, 21, 6, 3077–3091, 2025. DOI: 10.1021/acs.jctc.4c01491; also available as arXiv:2410.11392.
V. Vinod and P. Zaspel. Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties. Machine Learning: Science and Technology, 5, 045005, 2024. DOI: 10.1088/2632-2153/ad7f25; also available as arXiv:2407.17087.
V. Vinod, U. Kleinekathöfer and P. Zaspel. Optimized multifidelity machine learning for quantum chemistry. Machine Learning: Science and Technology, 5, 015054, 2024. DOI: 10.1088/2632-2153/ad2cef; also available as arXiv:2312.05661.
V. Vinod, S. Maity, P. Zaspel and U. Kleinekathöfer. Multifidelity Machine Learning for Molecular Excitation Energies. Journal of Chemical Theory and Computation, 19, 21, 7658–7670, 2023. DOI: 10.1021/acs.jctc.3c00882; also available as arXiv:2305.11292.
D. Maharjan and P. Zaspel. Toward data-driven filters in Paraview. Journal of Flow Visualization and Image Processing, 29(3):55-72, 2022. DOI: 10.1615/JFlowVisImageProc.2022040189.
H. Harbrecht, J.D. Jakeman and P. Zaspel. Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration. Communications in Computational Physics. 29 (4). 1152-1185, 2021. DOI: 10.4208/cicp.OA-2020-0060.
P. Zaspel, B. Huang, H. Harbrecht and O. A. von Lilienfeld. Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited. Journal of Chemical Theory and Computation, 15(3):1546-1559, 2019. DOI: 10.1021/acs.jctc.8b00832; also available as arXiv:1808.02799.
M. Griebel, Ch. Rieger, and P. Zaspel. Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations. International Journal for Uncertainty Quantification, 9(5):471-492 2019. DOI: 10.1615/Int.J.UncertaintyQuantification.2019029228; also available as arXiv:1810.11270.
P. Zaspel. Ensemble Kalman filters for reliability estimation in perfusion inference. International Journal for Uncertainty Quantification, 9(1):15-32, 2019. DOI: 10.1615/Int.J.UncertaintyQuantification.2018024865; also available as arXiv:1810.09290.
H. Harbrecht and P. Zaspel. On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems. Journal of Scientific Computing, Springer, 78(2):1272-1290, 2019. DOI: 10.1007/s10915-018-0807-6; also available as arXiv:1801.10532.
P. Zaspel. Algorithmic patterns for H matrices on many-core processors. Journal of Scientific Computing, Springer, 78(2):1174-1206, 2019. DOI: 10.1007/s10915-018-0809-4; also available as Preprint 2017-12, Fachbereich Mathematik, Universität Basel, Switzerland, 2017 and as arXiv:1708.09707 preprint.
P. Zaspel. Subspace correction methods in algebraic multi-level frames. Linear Algebra and its Applications, Vol. 488(1), Jan. 2016, pp. 505-521. DOI: 10.1016/j.laa.2015.09.026.
D. Pflüger, H.-J. Bungartz, M. Griebel, F. Jenko, T. Dannert, M. Heene, A. Parra Hinojosa, C. Kowitz and P. Zaspel: EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond. In: Lopes L. et al. (eds) Euro-Par 2014: Parallel Processing Workshops. Euro-Par 2014. Lecture Notes in Computer Science, vol 8806. Springer, Cham, 2014. DOI: 10.1007/978-3-319-14313-2_48.
P. Zaspel and M. Griebel. Solving incompressible two-phase flows on multi-GPU clusters. Computer & Fluids, 80(0):356 – 364, 2013. DOI: 10.1016/j.compfluid.2012.01.021.
P. Zaspel and M. Griebel. Massively parallel fluid simulations on Amazon’s HPC cloud. In Network Cloud Computing and Applications (NCCA), 2011 First International Symposium on, pages 73 -78, Nov. 2011. DOI: 10.1109/NCCA.2011.19.
P. Zaspel and M. Griebel. Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver. Computing and Visualization in Science, 14(8):371-383, 2011. DOI:10.1007/s00791-013-0188-1.
M. Griebel and P. Zaspel. A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations. Computer Science – Research and Development, 25(1-2):65-73, May 2010. DOI: 10.1007/s00450-010-0111-7.
Datasets
V. Vinod and P. Zaspel. QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules (1.1.0) [Data set]. Zenodo. 2024. https://zenodo.org/records/13925688.
Edited Volumes
V. Heuveline, M. Schick, C. Webster and P. Zaspel. Uncertainty Quantification and High Performance Computing, Dagstuhl Reports, Vol. 6, Issue 9, pp. 59-73.
Manuscripts
H. Harbrecht and P. Zaspel. A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters. Preprint 2018-11, Fachbereich Mathematik, Universität Basel, Switzerland, 2018. Also available as arXiv:1806.11558.
P. Zaspel. Analysis and parallelization strategies for Ruge-Stüben AMG on many-core processors, Preprint 2017-06, Fachbereich Mathematik, Universität Basel, Switzerland, 2017.