V. Vinod, P. Zaspel. LFaB: Low-fidelity as Bias for Active Learning in the chemical configuration space, arXiv preprint arXiv:2508.15577, https:\/\/doi.org\/10.48550\/arXiv.2508.15577<\/a><\/p>\n\n\n\n P. Zaspel and M. G\u00fcnther, “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<\/a>.<\/p>\n\n\n\n K. Shaju, T. Laepple, N. Hirsch, and P. Zaspel: Ice borehole thermometry: sensor placement using greedy optimal sampling<\/a>, Geosci. Instrum. Method. Data Syst., 14, 459\u2013474, https:\/\/doi.org\/10.5194\/gi-14-459-2025<\/a>, 2025.<\/p>\n\n\n\n M. Holzenkamp, \u200b\u200bD. Lyu, U. Kleinekath\u00f6fer and P. Zaspel: “Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials<\/a>.”,Machine Learning: Science and Technology, 2025. DOI: 10.1088\/2632-2153\/ae09ef<\/a>; also available as arXiv.2410.20398.<\/a><\/p>\n\n\n\n D. Lyu, M. Holzenkamp, \u200b\u200bV. Vinod, YM Holtkamp, \u200b\u200bS. Maity, C.R. Salazar, U. Kleinekath\u00f6fer and P. Zaspel, “Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning<\/a>.”, 2024. arXiv:2410.20551, accepted for publication in Advanced Theory and Simulations, May 2025.<\/p>\n\n\n\n V. Vinod and P. Zaspel. Benchmarking data efficiency in \u0394<\/em>-ML and multifidelity models for quantum chemistry<\/a>. J. Chem. Phys. 163, 024134, 2025<\/em>. DOI: https:\/<\/a>\/doi.org\/10.1063\/5.0272457<\/a>; also available as arXiv:2410.11391<\/em><\/a>.<\/p>\n\n\n\n V. Vinod, and P. Zaspel. QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules<\/a>. Sci Data<\/em> 12, 202, 2025. DOI: https:\/\/doi.org\/10.1038\/s41597-024-04247-3<\/a>; also available as arXiv:2406.14149<\/em><\/a>.<\/p>\n\n\n\n V. Vinod, D. Lyu, M. Ruth, U. Kleinekath\u00f6fer, P. R. Schreiner, and P. Zaspel. Predicting molecular energies of small organic molecules with multifidelity methods. <\/a>J. Comput. Chem., 46: e70056, 2025. DOI: https:\/\/doi.org\/10.1002\/jcc.70056<\/a>; also available as chemrxiv-2024-9zz16<\/a><\/em>.<\/p>\n\n\n\n V. Vinod and P. Zaspel. Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies<\/a>. J. Chem. Theory Comput, 21, 6, 3077\u20133091, 2025. DOI: 10.1021\/acs.jctc.4c01491<\/a>; also available as arXiv:2410.11392<\/a><\/em>.<\/p>\n\n\n\n V. Vinod and P. Zaspel. Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties<\/a>. Machine Learning: Science and Technology, 5, 045005, 2024. DOI: 10.1088\/2632-2153\/ad7f25<\/a>; also available as arXiv:<\/em>2407.17087.<\/p>\n\n\n\n V. Vinod, U. Kleinekath\u00f6fer and P. Zaspel. Optimized multifidelity machine learning for quantum chemistry.<\/a> Machine Learning: Science and Technology, 5, 015054, 2024. DOI: 10.1088\/2632-2153\/ad2cef<\/a>; also available as arXiv:2312.05661.<\/a><\/p>\n\n\n\n V. Vinod, S. Maity, P. Zaspel and U. Kleinekath\u00f6fer. Multifidelity Machine Learning for Molecular Excitation Energies.<\/a> Journal of Chemical Theory and Computation, 19, 21, 7658\u20137670, 2023. DOI: 10.1021\/acs.jctc.3c00882<\/a>; also available as arXiv:2305.11292<\/a>.<\/p>\n\n\n\n D. Maharjan and P. Zaspel. Toward data-driven filters in Paraview<\/a>. Journal of Flow Visualization and Image Processing, 29(3):55-72, 2022. DOI: 10.1615\/JFlowVisImageProc.2022040189<\/a>.<\/p>\n\n\n\n H. Harbrecht, J.D. Jakeman and P. Zaspel. Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration<\/a>. Communications in Computational Physics. 29<\/em> (4). 1152-1185, 2021. DOI: 10.4208\/cicp.OA-2020-0060<\/a>.<\/p>\n\n\n\n P. Zaspel, B. Huang, H. Harbrecht and O. A. von Lilienfeld. Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited<\/a>. Journal of Chemical Theory and Computation, 15(3):1546-1559, 2019. DOI: 10.1021\/acs.jctc.8b00832<\/a>; also available as arXiv:1808.02799<\/a>.<\/p>\n\n\n\n M. Griebel, Ch. Rieger, and P. Zaspel. Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations<\/a>. International Journal for Uncertainty Quantification, 9(5):471-492 2019. DOI: 10.1615\/Int.J.UncertaintyQuantification.2019029228; also available as arXiv:1810.11270<\/a>.<\/p>\n\n\n\n P. Zaspel. Ensemble Kalman filters for reliability estimation in perfusion inference<\/a>. International Journal for Uncertainty Quantification, 9(1):15-32, 2019. DOI: 10.1615\/Int.J.UncertaintyQuantification.2018024865; also available as arXiv:1810.09290<\/a>.<\/p>\n\n\n\n H. Harbrecht and P. Zaspel. On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems<\/a>. Journal of Scientific Computing, Springer, 78(2):1272-1290, 2019. DOI: 10.1007\/s10915-018-0807-6; also available as arXiv:1801.10532.<\/a><\/p>\n\n\n\n P. Zaspel. Algorithmic patterns for H matrices on many-core processors<\/a>. Journal of Scientific Computing, Springer, 78(2):1174-1206, 2019. DOI: 10.1007\/s10915-018-0809-4; also available as Preprint 2017-12<\/a>, Fachbereich Mathematik, Universit\u00e4t Basel, Switzerland, 2017 and as arXiv:1708.09707<\/a> preprint.<\/p>\n\n\n\n P. Zaspel. Subspace correction methods in algebraic multi-level frames<\/a>. Linear Algebra and its Applications, Vol. 488(1), Jan. 2016, pp. 505-521. DOI: 10.1016\/j.laa.2015.09.026.<\/p>\n\n\n\n D. Pfl\u00fcger, 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<\/a>. 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>\n\n\n\n P. Zaspel and M. Griebel. Solving incompressible two-phase flows on multi-GPU clusters<\/a>. Computer & Fluids<\/em>, 80(0):356 – 364, 2013. DOI: 10.1016\/j.compfluid.2012.01.021.<\/p>\n\n\n\n P. Zaspel and M. Griebel. Massively parallel fluid simulations on Amazon’s HPC cloud<\/a>. In Network Cloud Computing and Applications (NCCA), 2011 First International Symposium on<\/em>, pages 73 -78, Nov. 2011. DOI: 10.1109\/NCCA.2011.19<\/a>.<\/p>\n\n\n\n P. Zaspel and M. Griebel. Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver.<\/a> Computing and Visualization in Science<\/em>, 14(8):371-383, 2011. DOI:10.1007\/s00791-013-0188-1.<\/p>\n\n\n\n M. Griebel and P. Zaspel. A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations.<\/a> Computer Science – Research and Development,<\/em> 25(1-2):65-73, May 2010. DOI: 10.1007\/s00450-010-0111-7.<\/p>\n\n\n\n K. Shaju.: Ice borehole thermometry: Sensor placement using greedy optimal sampling, Zenodo [code, data set], https:\/\/doi.org\/10.5281\/zenodo.17849760<\/a>, 2025.<\/p>\n\n\n\n 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<\/a>. <\/p>\n\n\n\n V. Heuveline, M. Schick, C. Webster and P. Zaspel. Uncertainty Quantification and High Performance Computing, Dagstuhl Reports, Vol. 6, Issue 9, pp. 59-73.<\/p>\n\n\n\n H. Harbrecht and P. Zaspel. A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters.<\/a> Preprint 2018-11, Fachbereich Mathematik, Universit\u00e4t Basel, Switzerland, 2018. Also available as arXiv:1806.11558<\/a>.<\/p>\n\n\n\nPeer-Reviewed Articles<\/h4>\n\n\n\n
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