{"id":24,"date":"2020-05-31T17:32:03","date_gmt":"2020-05-31T17:32:03","guid":{"rendered":"http:\/\/mlhpc2.peter-zaspel.de\/?page_id=24"},"modified":"2026-07-16T13:42:35","modified_gmt":"2026-07-16T13:42:35","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.peter-zaspel.de\/?page_id=24","title":{"rendered":"Publications"},"content":{"rendered":"\n<p>Explore by category:<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-space-between is-layout-flex wp-container-core-buttons-is-layout-3d213aab wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link wp-element-button\" href=\"#preprints\">Preprints<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--2\"><a class=\"wp-block-button__link wp-element-button\" href=\"#peer-peviewed-articles\">Peer-Reviewed Articles<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--3\"><a class=\"wp-block-button__link wp-element-button\" href=\"#datasets\">Datasets<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--4\"><a class=\"wp-block-button__link wp-element-button\" href=\"#edited-volumes\">Edited Volumes<\/a><\/div>\n\n\n\n<div class=\"wp-block-button is-style-outline is-style-outline--5\"><a class=\"wp-block-button__link wp-element-button\" href=\"#manuscripts\">Manuscripts<\/a><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"preprints\">Preprints<\/h3>\n\n\n\n<p>L. Schaub and P. Zaspel. \u201cVariational Free Energy Pivot Selection for Pivoted Cholesky\u201d, 2026.<br>arXiv preprint: arXiv:2606.01821, DOI:<a href=\"https:\/\/doi.org\/10.48550\/arXiv.2606.01821\">10.48550\/arXiv.2606.01821<\/a>.<\/p>\n\n\n\n<p>S. Maity, V. Vinod, P. Zaspel and U. Kleinekath\u00f6fer. &#8220;\u2206-Machine Learning for LC-DFT-level Excitation Energies of Bacteriochlorophyll Molecules in a LH2 Complex&#8221;, 2026.<br>ChemRxiv preprint: chemrxiv.15002714, DOI: <a href=\"https:\/\/doi.org\/10.26434\/chemrxiv.15002714\/v1\">10.26434\/chemrxiv.15002714\/v1<\/a>.<\/p>\n\n\n\n<p>V. Vinod and P. Zaspel. \u201cImprovise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning\u201d, 2026.<br>arXiv preprint arXiv:2606.02662,&nbsp;DOI: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2606.02662\">10.48550\/arXiv.2606.02662<\/a>.<\/p>\n\n\n\n<p>P. Zaspel and M. G\u00fcnther. &#8220;Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes&#8221;, 2024.<br>arXiv preprint arxiv:2406.18726, DOI: <a href=\"https:\/\/doi.org\/10.48550\/arXiv.2406.18726\">10.48550\/arXiv.2406.18726<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"peer-peviewed-articles\">Peer-Reviewed Articles<\/h3>\n\n\n\n<div data-wp-context=\"{ &quot;autoclose&quot;: true, &quot;accordionItems&quot;: [] }\" data-wp-interactive=\"core\/accordion\" role=\"group\" class=\"wp-block-accordion is-layout-flow wp-block-accordion-is-layout-flow\">\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-6&quot;, &quot;openByDefault&quot;: true }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-open is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"true\" aria-controls=\"accordion-item-6-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-6\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2026<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div aria-labelledby=\"accordion-item-6\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-6-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>V. Vinod and P. Zaspel. &#8220;LFaB: Low-fidelity as Bias for Active Learning in the chemical configuration space&#8221;, 2026. <br>Journal of Chemical Theory and Computation, 2026, 22, 11, 5637-5648. DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jctc.6c00009\">10.1021\/acs.jctc.6c00009<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-7&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-7-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-7\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2025<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-7\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-7-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>K. Shaju, T. Laepple, N. Hirsch, and P. Zaspel. &#8220;Ice borehole thermometry: sensor placement using greedy optimal sampling&#8221;, 2025.<br>Geoscientific Instrumentation, Methods and Data Systems, 14, 459\u2013474. DOI: <a href=\"https:\/\/doi.org\/10.5194\/gi-14-459-2025\">10.5194\/gi-14-459-2025<\/a>.<\/p>\n\n\n\n<p>M. Holzenkamp, \u200b\u200bD. Lyu, U. Kleinekath\u00f6fer and P. Zaspel. &#8220;Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials.&#8221;, 2025.<br>Machine Learning: Science and Technology, 6 045019. DOI: <a href=\"https:\/\/doi.org\/10.1088\/2632-2153\/ae09ef\" data-type=\"URL\" data-id=\"10.1088\/2632-2153\/ae09ef\">10.1088\/2632-2153\/ae09ef<\/a>.<\/p>\n\n\n\n<p>D. Lyu, \u200b\u200bV. Vinod, M. Holzenkamp, YM Holtkamp, \u200b\u200bS. Maity, C.R. Salazar, U. Kleinekath\u00f6fer and P. Zaspel. \u201cExcitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A study employing Multifidelity Machine Learning\u201d, 2025.<br>Advanced Theory and Simulations 8, no. 11 (2025): e00271. DOI: <a href=\"https:\/\/doi.org\/10.1002\/adts.202500271\">10.1002\/adts.202500271<\/a>.<\/p>\n\n\n\n<p>V. Vinod and P. Zaspel. &#8220;Benchmarking data efficiency in <em>\u0394<\/em>-ML and multifidelity models for quantum chemistry&#8221;, 2025. <br>Journal of Chemical Physics 163, 024134. DOI: <a href=\"https:\/\/doi.org\/10.1063\/5.0272457\">10.1063\/5.0272457<\/a>.<\/p>\n\n\n\n<p>V. Vinod and P. Zaspel. &#8220;QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules&#8221;, 2025. <br>Scientific Data 12, 202. DOI: <a href=\"https:\/\/doi.org\/10.1038\/s41597-024-04247-3\">10.1038\/s41597-024-04247-3<\/a>.<\/p>\n\n\n\n<p>V. Vinod, D. Lyu, M. Ruth, U. Kleinekath\u00f6fer, P. R. Schreiner and P. Zaspel. &#8220;Predicting molecular energies of small organic molecules with multifidelity methods&#8221;, 2025. <br>Journal of Computational Chemistry, 46: e70056. DOI: <a href=\"https:\/\/doi.org\/10.1002\/jcc.70056\" data-type=\"URL\" data-id=\"https:\/\/doi.org\/10.1002\/jcc.70056\">10.1002\/jcc.70056<\/a>.<\/p>\n\n\n\n<p>V. Vinod and P. Zaspel. &#8220;Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies&#8221;, 2025.<br>Journal of Chemical Theory and Computation,  21, 6, 3077\u20133091. DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jctc.4c01491\">10.1021\/acs.jctc.4c01491<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-8&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-8-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-8\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2024<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-8\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-8-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>V. Vinod and P. Zaspel. &#8220;Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties&#8221;, 2024.<br>Machine Learning: Science and Technology, 5, 045005. DOI: <a href=\"http:\/\/doi.org\/10.1088\/2632-2153\/ad7f25\">10.1088\/2632-2153\/ad7f25<\/a>.<\/p>\n\n\n\n<p>V. Vinod, U. Kleinekath\u00f6fer and P. Zaspel. &#8220;Optimized multifidelity machine learning for quantum chemistry&#8221;, 2024.<br>Machine Learning: Science and Technology, 5, 015054. DOI: <a href=\"https:\/\/doi.org\/10.1088\/2632-2153\/ad2cef\" data-type=\"URL\">10.1088\/2632-2153\/ad2cef<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-9&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-9-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-9\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2023<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-9\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-9-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>V. Vinod, S. Maity, P. Zaspel and U. Kleinekath\u00f6fer. &#8220;Multifidelity Machine Learning for Molecular Excitation Energies&#8221;, 2023. <br>Journal of Chemical Theory and Computation, 19, 21, 7658\u20137670. DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jctc.3c00882\" data-type=\"URL\">10.1021\/acs.jctc.3c00882<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-10&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-10-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-10\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2022<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-10\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-10-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>D. Maharjan and P. Zaspel. &#8220;Toward data-driven filters in Paraview&#8221;, 2022.<br>Journal of Flow Visualization and Image Processing, 29(3):55-72. DOI: <a href=\"https:\/\/doi.org\/10.1615\/JFlowVisImageProc.2022040189\">10.1615\/JFlowVisImageProc.2022040189<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-11&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-11-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-11\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2021<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-11\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-11-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>H. Harbrecht, J.D. Jakeman and P. Zaspel. &#8220;Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration&#8221;, 2021. Communications in Computational Physics. <em>29<\/em> (4). 1152-1185. DOI: <a href=\"https:\/\/doi.org\/10.4208\/cicp.OA-2020-0060\">10.4208\/cicp.OA-2020-0060<\/a>.<\/p>\n<\/div>\n<\/div>\n\n\n\n<div data-wp-class--is-open=\"state.isOpen\" data-wp-context=\"{ &quot;id&quot;: &quot;accordion-item-12&quot;, &quot;openByDefault&quot;: false }\" data-wp-init=\"callbacks.initAccordionItems\" data-wp-on-window--hashchange=\"callbacks.hashChange\" class=\"wp-block-accordion-item is-layout-flow wp-block-accordion-item-is-layout-flow\">\n<h3 class=\"wp-block-accordion-heading\"><button aria-expanded=\"false\" aria-controls=\"accordion-item-12-panel\" data-wp-bind--aria-expanded=\"state.isOpen\" data-wp-on--click=\"actions.toggle\" data-wp-on--keydown=\"actions.handleKeyDown\" id=\"accordion-item-12\" type=\"button\" class=\"wp-block-accordion-heading__toggle\"><span class=\"wp-block-accordion-heading__toggle-title\">2010 &#8211; 2020<\/span><span class=\"wp-block-accordion-heading__toggle-icon\" aria-hidden=\"true\">+<\/span><\/button><\/h3>\n\n\n\n<div inert aria-labelledby=\"accordion-item-12\" data-wp-bind--inert=\"!state.isOpen\" id=\"accordion-item-12-panel\" role=\"region\" class=\"wp-block-accordion-panel is-layout-flow wp-block-accordion-panel-is-layout-flow\">\n<p>P. Zaspel, B. Huang, H. Harbrecht and O. A. von Lilienfeld. &#8220;Boosting quantum machine learning models with multi-level combination technique: People diagrams revisited&#8221;, 2019. <br>Journal of Chemical Theory and Computation, 15(3):1546-1559. DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jctc.8b00832\">10.1021\/acs.jctc.8b00832<\/a>.<\/p>\n\n\n\n<p>M. Griebel, Ch. Rieger, and P. Zaspel. &#8220;Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations&#8221;, 2019. <br>International Journal for Uncertainty Quantification, 9(5):471-492. DOI: <a href=\"https:\/\/www.doi.org\/10.1615\/Int.J.UncertaintyQuantification.2019029228\" data-type=\"link\" data-id=\"https:\/\/www.doi.org\/10.1615\/Int.J.UncertaintyQuantification\">10.1615\/Int.J.UncertaintyQuantification.2019029228<\/a>.<\/p>\n\n\n\n<p>P. Zaspel. &#8220;Ensemble Kalman filters for reliability estimation in perfusion inference&#8221;, 2019. <br>International Journal for Uncertainty Quantification, 9(1):15-32, DOI: <a href=\"https:\/\/www.doi.org\/10.1615\/Int.J.UncertaintyQuantification.2018024865\" data-type=\"link\" data-id=\"https:\/\/www.doi.org\/10.1615\/Int.J.UncertaintyQuantification.2018024865\">10.1615\/Int.J.UncertaintyQuantification.2018024865<\/a>.<\/p>\n\n\n\n<p>H. Harbrecht and P. Zaspel. &#8220;On the algebraic construction of sparse multilevel approximations of elliptic tensor product problems&#8221;, 2019.<br>Journal of Scientific Computing, 78(2):1272-1290. DOI: <a href=\"https:\/\/www.doi.org\/10.1007\/s10915-018-0807-6\">10.1007\/s10915-018-0807-6<\/a><a href=\"https:\/\/arxiv.org\/abs\/1801.10532\" target=\"_blank\" rel=\"noreferrer noopener\">.<\/a><\/p>\n\n\n\n<p>P. Zaspel. &#8220;Algorithmic patterns for H matrices on many-core processors&#8221;, 2019. <br>Journal of Scientific Computing, Springer, 78(2):1174-1206. DOI: <a href=\"https:\/\/doi.org\/10.1007\/s10915-018-0809-4\" data-type=\"link\" data-id=\"https:\/\/doi.org\/10.1007\/s10915-018-0809-4\">10.1007\/s10915-018-0809-4.<\/a><\/p>\n\n\n\n<p>P. Zaspel. &#8220;Subspace correction methods in algebraic multi-level frames&#8221;, 2016. <br>Linear Algebra and its Applications, Vol. 488(1),&nbsp; Jan. 2016, pp. 505-521. DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.laa.2015.09.026\">10.1016\/j.laa.2015.09.026<\/a>.<\/p>\n\n\n\n<p>D.&nbsp;Pfl\u00fcger, H.-J.&nbsp;Bungartz, M.&nbsp;Griebel, F.&nbsp;Jenko, T.&nbsp;Dannert, M.&nbsp;Heene, A.&nbsp;Parra Hinojosa, C.&nbsp;Kowitz and P.&nbsp;Zaspel. <br>&#8220;EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond&#8221;, 2014. <br>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: <a href=\"https:\/\/doi.org\/10.1007\/978-3-319-14313-2_48\">10.1007\/978-3-319-14313-2_48.<\/a><\/p>\n\n\n\n<p>P.&nbsp;Zaspel and M.&nbsp;Griebel. &#8220;Solving incompressible two-phase flows on multi-GPU clusters&#8221;, 2013. <br><em>Computer &amp; Fluids<\/em>, 80(0):356 &#8211; 364, 2013. DOI: <a href=\"https:\/\/www.doi.org\/10.1016\/j.compfluid.2012.01.021\">10.1016\/j.compfluid.2012.01.021<\/a>.<\/p>\n\n\n\n<p>P.&nbsp;Zaspel and M.&nbsp;Griebel. &#8220;Massively parallel fluid simulations on Amazon&#8217;s HPC cloud&#8221;, 2011.<br>Network Cloud Computing and Applications (NCCA), 2011 First International Symposium on, pages 73 -78, Nov. 2011. DOI: <a href=\"https:\/\/doi.org\/10.1109\/NCCA.2011.19\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/NCCA.2011.19<\/a>.<\/p>\n\n\n\n<p>P.&nbsp;Zaspel and M.&nbsp;Griebel. &#8220;Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver&#8221;, 2011.<br>Computing and Visualization in Science, 14(8):371-383, 2011. DOI: <a href=\"https:\/\/www.doi.org\/10.1007\/s00791-013-0188-1\">10.1007\/s00791-013-0188-1<\/a>.<\/p>\n\n\n\n<p>M.&nbsp;Griebel and P.&nbsp;Zaspel. &#8220;A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations&#8221;, 2010.<br>Computer Science &#8211; Research and Development, 25(1-2):65-73, May 2010. DOI: <a href=\"https:\/\/www.doi.org\/10.1007\/s00791-013-0188-1\">10.1007\/s00450-010-0111-7<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"datasets\">Datasets<\/h3>\n\n\n\n<p>K. Shaju. &#8220;Ice borehole thermometry: Sensor placement using greedy optimal sampling&#8221;, 2025.<br>Zenodo [code, data set], DOI: <a href=\"https:\/\/doi.org\/10.5281\/zenodo.17849760\">10.5281\/zenodo.17849760<\/a>.<\/p>\n\n\n\n<p>V. Vinod and P. Zaspel. &#8220;QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules (1.1.0)&#8221;, 2024.<br>Zenodo [data set]. DOI: <a href=\"https:\/\/doi.org\/10.5281\/zenodo.13925688\">10.5281\/zenodo.13925688<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"edited-volumes\">Edited Volumes<\/h3>\n\n\n\n<p>V. Heuveline, M. Schick, C. Webster and P. Zaspel. &#8220;Uncertainty Quantification and High Performance Computing&#8221;, 2016.<br>Dagstuhl Reports, Vol. 6, Issue 9, pp. 59-73. DOI: <a href=\"https:\/\/doi.org\/10.4230\/DagRep.6.9.59\">10.4230\/DagRep.6.9.59<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"manuscripts\">Manuscripts<\/h3>\n\n\n\n<p>H. Harbrecht and P. Zaspel. &#8220;A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters&#8221;, 2018. <br>Preprint 2018-11, Fachbereich Mathematik, Universit\u00e4t Basel, Switzerland. DOI: <a href=\"https:\/\/dx.doi.org\/10.5451\/unibas-ep70080\">10.5451\/unibas-ep70080<\/a>.<\/p>\n\n\n\n<p>P. Zaspel. &#8220;Analysis and parallelization strategies for Ruge-St\u00fcben AMG on many-core processors&#8221;, 2017.<br>Preprint 2017-06, Fachbereich Mathematik, Universit\u00e4t Basel, Switzerland. DOI: <a href=\"https:\/\/dx.doi.org\/10.5451\/unibas-ep69936\">10.5451\/unibas-ep69936<\/a>.<br><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explore by category: Preprints L. Schaub and P. Zaspel. \u201cVariational Free Energy Pivot Selection for Pivoted Cholesky\u201d, 2026.arXiv preprint: arXiv:2606.01821, DOI:10.48550\/arXiv.2606.01821. S. Maity, V. Vinod, P. Zaspel and U. Kleinekath\u00f6fer. &#8220;\u2206-Machine Learning for LC-DFT-level Excitation Energies of Bacteriochlorophyll Molecules in<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","_mc_calendar":[],"footnotes":""},"class_list":["post-24","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/24","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=24"}],"version-history":[{"count":113,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/24\/revisions"}],"predecessor-version":[{"id":1856,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/24\/revisions\/1856"}],"wp:attachment":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}