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Preprints
L. Schaub and P. Zaspel. “Variational Free Energy Pivot Selection for Pivoted Cholesky”, 2026.
arXiv preprint: arXiv:2606.01821, 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”, 2026.
ChemRxiv preprint: chemrxiv.15002714, DOI: 10.26434/chemrxiv.15002714/v1.
V. Vinod and P. Zaspel. “Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning”, 2026.
arXiv preprint arXiv:2606.02662, DOI: 10.48550/arXiv.2606.02662.
P. Zaspel and M. Günther. “Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes”, 2024.
arXiv preprint arxiv:2406.18726, 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”, 2026.
Journal of Chemical Theory and Computation, 2026, 22, 11, 5637-5648. DOI: 10.1021/acs.jctc.6c00009.
K. Shaju, T. Laepple, N. Hirsch, and P. Zaspel. “Ice borehole thermometry: sensor placement using greedy optimal sampling”, 2025.
Geoscientific Instrumentation, Methods and Data Systems, 14, 459–474. 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.”, 2025.
Machine Learning: Science and Technology, 6 045019. 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”, 2025.
Advanced Theory and Simulations 8, no. 11 (2025): e00271. DOI: 10.1002/adts.202500271.
V. Vinod and P. Zaspel. “Benchmarking data efficiency in Δ-ML and multifidelity models for quantum chemistry”, 2025.
Journal of Chemical Physics 163, 024134. DOI: 10.1063/5.0272457.
V. Vinod and P. Zaspel. “QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules”, 2025.
Scientific Data 12, 202. 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”, 2025.
Journal of Computational Chemistry, 46: e70056. DOI: 10.1002/jcc.70056.
V. Vinod and P. Zaspel. “Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies”, 2025.
Journal of Chemical Theory and Computation, 21, 6, 3077–3091. DOI: 10.1021/acs.jctc.4c01491.
V. Vinod and P. Zaspel. “Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties”, 2024.
Machine Learning: Science and Technology, 5, 045005. DOI: 10.1088/2632-2153/ad7f25.
V. Vinod, U. Kleinekathöfer and P. Zaspel. “Optimized multifidelity machine learning for quantum chemistry”, 2024.
Machine Learning: Science and Technology, 5, 015054. DOI: 10.1088/2632-2153/ad2cef.
V. Vinod, S. Maity, P. Zaspel and U. Kleinekathöfer. “Multifidelity Machine Learning for Molecular Excitation Energies”, 2023.
Journal of Chemical Theory and Computation, 19, 21, 7658–7670. DOI: 10.1021/acs.jctc.3c00882.
D. Maharjan and P. Zaspel. “Toward data-driven filters in Paraview”, 2022.
Journal of Flow Visualization and Image Processing, 29(3):55-72. 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”, 2021. Communications in Computational Physics. 29 (4). 1152-1185. 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”, 2019.
Journal of Chemical Theory and Computation, 15(3):1546-1559. 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”, 2019.
International Journal for Uncertainty Quantification, 9(5):471-492. DOI: 10.1615/Int.J.UncertaintyQuantification.2019029228.
P. Zaspel. “Ensemble Kalman filters for reliability estimation in perfusion inference”, 2019.
International Journal for Uncertainty Quantification, 9(1):15-32, 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”, 2019.
Journal of Scientific Computing, 78(2):1272-1290. DOI: 10.1007/s10915-018-0807-6.
P. Zaspel. “Algorithmic patterns for H matrices on many-core processors”, 2019.
Journal of Scientific Computing, Springer, 78(2):1174-1206. DOI: 10.1007/s10915-018-0809-4.
P. Zaspel. “Subspace correction methods in algebraic multi-level frames”, 2016.
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”, 2014.
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”, 2013.
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”, 2011.
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”, 2011.
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”, 2010.
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”, 2025.
Zenodo [code, data set], DOI: 10.5281/zenodo.17849760.
V. Vinod and P. Zaspel. “QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules (1.1.0)”, 2024.
Zenodo [data set]. DOI: 10.5281/zenodo.13925688.
Edited Volumes
V. Heuveline, M. Schick, C. Webster and P. Zaspel. “Uncertainty Quantification and High Performance Computing”, 2016.
Dagstuhl Reports, Vol. 6, Issue 9, pp. 59-73. 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”, 2018.
Preprint 2018-11, Fachbereich Mathematik, Universität Basel, Switzerland. DOI: 10.5451/unibas-ep70080.
P. Zaspel. “Analysis and parallelization strategies for Ruge-Stüben AMG on many-core processors”, 2017.
Preprint 2017-06, Fachbereich Mathematik, Universität Basel, Switzerland. DOI: 10.5451/unibas-ep69936.