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Preprints
L. Schaub and P. Zaspel, “Variational Free Energy Pivot Selection for Pivoted Cholesky”,
arXiv preprint: arXiv:2606.01821, 2026, DOI:10.48550/arXiv.2606.01821.
S. Maity, V. Vinod, P. Zaspel and U. Kleinekathöfer, “∆-Machine Learning for LC-DFT-level Excitation Energies of Bacteriochlorophyll Molecules in a LH2 Complex”,
ChemRxiv preprint: chemrxiv.15002714, 2026, DOI: 10.26434/chemrxiv.15002714/v1.
V. Vinod and P. Zaspel, “Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning”,
arXiv preprint arXiv:2606.02662, 2026, DOI: 10.48550/arXiv.2606.02662.
P. Zaspel and M. Günther, “Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes”,
arXiv preprint arxiv:2406.18726, 2024, DOI: 10.48550/arXiv.2406.18726.
Peer-Reviewed Articles
V. Vinod and P. Zaspel, “LFaB: Low-fidelity as Bias for Active Learning in the chemical configuration space”,
Journal of Chemical Theory and Computation, 22, 11, 5637-5648, 2026, DOI: 10.1021/acs.jctc.6c00009.
K. Shaju, T. Laepple, N. Hirsch, and P. Zaspel, “Ice borehole thermometry: sensor placement using greedy optimal sampling”,
Geoscientific Instrumentation, Methods and Data Systems, 14, 459–474, 2025, DOI: 10.5194/gi-14-459-2025.
M. Holzenkamp, D. Lyu, U. Kleinekathöfer and P. Zaspel, “Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.”,
Machine Learning: Science and Technology, 6 045019, 2025, DOI: 10.1088/2632-2153/ae09ef.
D. Lyu, V. Vinod, M. Holzenkamp, 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”,
Advanced Theory and Simulations 8, no. 11: e00271, 2025, DOI: 10.1002/adts.202500271.
V. Vinod and P. Zaspel, “Benchmarking data efficiency in Δ-ML and multifidelity models for quantum chemistry”,
Journal of Chemical Physics 163, 024134, 2025, DOI: 10.1063/5.0272457.
V. Vinod and P. Zaspel, “QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules”,
Scientific Data 12, 202, 2025, DOI: 10.1038/s41597-024-04247-3.
V. Vinod, D. Lyu, M. Ruth, U. Kleinekathöfer, P. R. Schreiner and P. Zaspel, “Predicting molecular energies of small organic molecules with multifidelity methods”,
Journal of Computational Chemistry, 46: e70056, 2025, DOI: 10.1002/jcc.70056.
V. Vinod and P. Zaspel, “Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies”,
Journal of Chemical Theory and Computation, 21, 6, 3077–3091, 2025, DOI: 10.1021/acs.jctc.4c01491.
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.
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.
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.
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: People diagrams revisited”,
Journal of Chemical Theory and Computation, 15(3):1546-1559, 2019, DOI: 10.1021/acs.jctc.8b00832.
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.
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.
H. Harbrecht and P. Zaspel, “On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems”,
Journal of Scientific Computing, 78(2):1272-1290, 2019, DOI: 10.1007/s10915-018-0807-6.
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.
P. Zaspel, “Subspace correction methods in algebraic multi-level frames”,
Linear Algebra and its Applications, Vol. 488(1), pp. 505-521, 2016, 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”,
Lopes L. et al. (eds) Euro-Par 2014: Parallel Processing Workshops. 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”,
Network Cloud Computing and Applications (NCCA), First International Symposium on Network Cloud Computing and Applications, 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
K. Shaju. “Ice borehole thermometry: Sensor placement using greedy optimal sampling”,
Zenodo [code, data set], 2025, DOI: 10.5281/zenodo.17849760.
V. Vinod and P. Zaspel. “QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules (1.1.0)”,
Zenodo [data set], 2024, DOI: 10.5281/zenodo.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, 2016, DOI: 10.4230/DagRep.6.9.59.
Manuscripts
H. Harbrecht and P. Zaspel, “A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters”,
Preprint, Fachbereich Mathematik, Universität Basel, Switzerland, 2018, DOI: 10.5451/unibas-ep70080.
P. Zaspel, “Analysis and parallelization strategies for Ruge-Stüben AMG on many-core processors”,
Preprint, Fachbereich Mathematik, Universität Basel, Switzerland, 2017, DOI: 10.5451/unibas-ep69936.