{"id":22,"date":"2020-05-31T17:32:03","date_gmt":"2020-05-31T17:32:03","guid":{"rendered":"http:\/\/mlhpc2.peter-zaspel.de\/?page_id=22"},"modified":"2025-11-02T21:26:41","modified_gmt":"2025-11-02T21:26:41","slug":"research","status":"publish","type":"page","link":"https:\/\/www.peter-zaspel.de\/?page_id=22","title":{"rendered":"Research"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">Projects<\/h4>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.spp-antarktisforschung.uni-rostock.de\/en\/\"><strong>DFG project &#8220;ICEBAY &#8211; Temperature reconstruction combining boreholes thermometry and ice-cores with Bayesian hierarchical modelling &#8211; Global warming vs. natural variability in DML, Antarctica&#8221;<\/strong> (duration: 2026-2028)<br><em>Research project within the DFG SPP 1158 &#8220;Antarctic Research&#8221; (joint project with Thomas Laepple, Alfred Wegener Institut, Potsdam)<\/em><\/a><\/li><li><a href=\"https:\/\/www.uni-muenster.de\/SPP2363\/Projects\/projects.html\"><strong>DFG project &#8220;Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes&#8221;<\/strong> (duration 2026-2029)<\/a><br><a href=\"https:\/\/www.uni-muenster.de\/SPP2363\/Projects\/projects.html\"><em>Research project within the second phase of the DFG SPP 2363 &#8220;Utilization and Development of Machine Learning for Molecular Applications \u2013 Molecular Machine Learning&#8221; (joint project with Ulrich Kleinekath\u00f6fer, Physics, Constructor University Bremen)<\/em><\/a><\/li><li><a href=\"https:\/\/gepris.dfg.de\/gepris\/projekt\/556067584\"><strong>DFG subproject &#8220;Data-driven surrogate modelling for differential-algebraic port-Hamiltonian systems&#8221; (C03)<\/strong>  (duration: 2025-2028)<\/a><br><a href=\"https:\/\/gepris.dfg.de\/gepris\/projekt\/556067584\"><em>Subproject of the CRC 1701 &#8220;Port-Hamiltonian Systems&#8221; (co-PI together with Michael G\u00fcnther)<\/em><\/a><\/li><li><a href=\"https:\/\/gepris.dfg.de\/gepris\/projekt\/556069296\"><strong>DFG subproject &#8220;Benchmark platform for port-Hamiltonian systems&#8221; (S)<\/strong> (duration: 2025-2028)<br><em>Subproject of the CRC 1701 &#8220;Port-Hamiltonian Systems&#8221;  (co-PI together with Hanno Gottschalk)<\/em><\/a><\/li><li><a href=\"https:\/\/www.mardata.de\/research\/mardata-2nd-cohort-2021-2024\/digital-ice-cores-paleo-climate-reconstruction-using-bayesian-modeling\/\"><strong>MarDATA project \u201cDigital Ice Cores: Paleo-Climate reconstruction using Bayesian methods\u201d<\/strong> (duration: 2023-2026)<\/a><br><a href=\"https:\/\/www.mardata.de\/research\/mardata-2nd-cohort-2021-2024\/digital-ice-cores-paleo-climate-reconstruction-using-bayesian-modeling\/\"><em>Helmholtz School for Marine Data Science (joint project with Thomas Laepple, Alfred Wegener Institute &amp; U. Bremen).<\/em><\/a><\/li><li><a href=\"https:\/\/www.mardata.de\/research\/mardata-2nd-cohort-2021-2024\/bayesian-chronology-modelling-for-paleoclimate-archives\/\"><strong>MarDATA project &#8220;Bayesian Chronology Modelling for Paleoclimate Archives&#8221;<\/strong> (duration: 2022-2026)<br><em>Helmholtz School for Marine Data Science (joint project with Florian Adolphi, Alfred Wegener Institute)<\/em>.<\/a><\/li><li><a href=\"https:\/\/www.uni-muenster.de\/SPP2363\/Projects\/projects.html\"><strong>DFG project &#8220;Multi-fidelity, Active Learning Strategies for Exciton Transfer Among Adsorbed Molecules&#8221;<\/strong> (duration 2022-2026)<\/a><br><a href=\"https:\/\/www.uni-muenster.de\/SPP2363\/Projects\/projects.html\"><em>Research project within the first phase of the DFG SPP 2363 &#8220;Utilization and Development of Machine Learning for Molecular Applications \u2013 Molecular Machine Learning&#8221; (joint project with Ulrich Kleinekath\u00f6fer, Physics, Jacobs University Bremen)<\/em><\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Finished projects<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/gepris.dfg.de\/gepris\/projekt\/466761712\"><strong>DFG project &#8220;Excitation Energy Transfer in a Photosynthetic System with more than 100 Million Atoms&#8221; <\/strong>(duration: 2021-2025)<\/a><br><a href=\"https:\/\/gepris.dfg.de\/gepris\/projekt\/466761712\"><em>Individual Research Grant funded by DFG (joint project with Ulrich Kleinekath\u00f6fer, Physics, Constructor University Bremen)<\/em><\/a><\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Research interests<\/h4>\n\n\n\n<ul class=\"wp-block-list\"><li><strong><a href=\"https:\/\/www.peter-zaspel.de\/?page_id=84\">Machine Learning, Uncertainty Quantification and Big Data<\/a><\/strong><ul><li>multi-fidelity machine learning (e.g. by sparse grid combination technique)<\/li><li>approximate training (low-rank approximation by e.g. hierarchical matrices)<\/li><li>stochastic collocation, Bayesian inference \/ data assimilation<\/li><li>basic research wrt. reproducing kernel Hilbert spaces \/ Gaussian processes<\/li><\/ul><\/li><li><strong><a href=\"https:\/\/www.peter-zaspel.de\/?page_id=100\">High Performance Computing<\/a><\/strong><ul><li>numerics \/ algorithms for many-core processors (e.g. GPUs)<\/li><li>scalable distributed-memory parallel computing in machine learning and scientific computing<\/li><\/ul><\/li><li><strong><a href=\"https:\/\/www.peter-zaspel.de\/?page_id=470\" data-type=\"page\" data-id=\"470\">Interdisciplinary applications<\/a><\/strong><ul><li>material science, quantum chemistry (data from DFT, CC, etc.)<\/li><li>paleo-climate reconstruction (calibration, etc.)<\/li><li>fluid mechanics (two-phase flows, plasma physics)<\/li><li>medical imaging (dynamic contrast-enhanced imaging)<\/li><\/ul><\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"software\">Software<\/h4>\n\n\n\n<p>Find our recent software contributions for publications and beyond on our Github site <a href=\"https:\/\/github.com\/SM4DA\">https:\/\/github.com\/SM4DA<\/a>.<\/p>\n\n\n\n<p>In addition, our software developments include:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>hmglib<\/strong> &#8211; Open Source library for hierarchical matrices on GPU (<a href=\"https:\/\/github.com\/zaspel\/hmglib\">https:\/\/github.com\/zaspel\/hmglib<\/a>)<\/li><li><strong>MPLA<\/strong> &#8211; Open Source multi-GPU parallel library for dense iterative solvers (<a href=\"https:\/\/github.com\/zaspel\/MPLA\">https:\/\/github.com\/zaspel\/MPLA<\/a>)<\/li><li><strong>QML<\/strong> &#8211; Extension of the Open Source Quantum Machine Learning library by multi-fidelity techniques (for now hosted in the fork <a href=\"https:\/\/github.com\/zaspel\/qml\">https:\/\/github.com\/zaspel\/qml<\/a>)<\/li><li><strong>UQ on GPU<\/strong> &#8211; (Multi-) GPU parallel kernel-based methods for uncertainty quantification in random partial differential equations<\/li><li><strong>AMG on GPU<\/strong> &#8211; Hybrid GPU-based Ruge-St\u00fcben algebraic multigrid<\/li><li><strong>NaSt3DGPF<\/strong> &#8211; Multi-GPU MPI-parallel version of the fluid mechanics solver NaSt3DGPF (<a href=\"https:\/\/ins.uni-bonn.de\/media\/public\/u\/griebel\/NaSt3DGPF\/projects.html\">https:\/\/ins.uni-bonn.de\/media\/public\/u\/griebel\/NaSt3DGPF\/projects.html<\/a>)<\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Showcases<\/h4>\n\n\n\n<div class=\"alignnormal\"><div id=\"metaslider-id-57\" style=\"max-width: 500px; margin: 0 auto;\" class=\"ml-slider-3-23-0 metaslider metaslider-flex metaslider-57 ml-slider\">\n    <div id=\"metaslider_container_57\">\n        <div id=\"metaslider_57\">\n            <ul aria-live=\"polite\" class=\"slides\">\n                <li style=\"display: block; width: 100%;\" class=\"slide-58 ms-image\"><img loading=\"lazy\" decoding=\"async\" width=\"958\" height=\"714\" src=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/multifidelity.png\" class=\"slider-57 slide-58\" alt=\"\" rel=\"\" title=\"multifidelity\" style=\"margin-top: 12.734864300626%\" srcset=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/multifidelity.png 958w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/multifidelity-300x224.png 300w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/multifidelity-768x572.png 768w\" sizes=\"auto, (max-width: 958px) 100vw, 958px\" \/><div class=\"caption-wrap\"><div class=\"caption\">Machine learning<\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-59 ms-image\"><img loading=\"lazy\" decoding=\"async\" width=\"960\" height=\"655\" src=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/csm_perfusion_estimate_noisy_d5b8db7644.jpg\" class=\"slider-57 slide-59\" alt=\"\" rel=\"\" title=\"csm_perfusion_estimate_noisy_d5b8db7644\" style=\"margin-top: 15.885416666667%\" srcset=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/csm_perfusion_estimate_noisy_d5b8db7644.jpg 960w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/csm_perfusion_estimate_noisy_d5b8db7644-300x205.jpg 300w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/csm_perfusion_estimate_noisy_d5b8db7644-768x524.jpg 768w\" sizes=\"auto, (max-width: 960px) 100vw, 960px\" \/><div class=\"caption-wrap\"><div class=\"caption\">Bayesian inference<\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-60 ms-image\"><img loading=\"lazy\" decoding=\"async\" width=\"936\" height=\"918\" src=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/nast_gpu_weak_scaling.jpg\" class=\"slider-57 slide-60\" alt=\"\" rel=\"\" title=\"nast_gpu_weak_scaling\" style=\"margin-top: 0.96153846153846%\" srcset=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/nast_gpu_weak_scaling.jpg 936w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/nast_gpu_weak_scaling-300x294.jpg 300w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/nast_gpu_weak_scaling-768x753.jpg 768w\" sizes=\"auto, (max-width: 936px) 100vw, 936px\" \/><div class=\"caption-wrap\"><div class=\"caption\">High Performance Computing<\/div><\/div><\/li>\n                <li style=\"display: none; width: 100%;\" class=\"slide-62 ms-image\"><img loading=\"lazy\" decoding=\"async\" width=\"702\" height=\"648\" src=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/uq_two_phase_problem_result.jpg\" class=\"slider-57 slide-62\" alt=\"\" rel=\"\" title=\"uq_two_phase_problem_result\" style=\"margin-top: 3.8461538461538%\" srcset=\"https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/uq_two_phase_problem_result.jpg 702w, https:\/\/www.peter-zaspel.de\/wp-content\/uploads\/2020\/05\/uq_two_phase_problem_result-300x277.jpg 300w\" sizes=\"auto, (max-width: 702px) 100vw, 702px\" \/><div class=\"caption-wrap\"><div class=\"caption\">Uncertainty Quantification<\/div><\/div><\/li>\n            <\/ul>\n        <\/div>\n        \n    <\/div>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Projects DFG project &#8220;ICEBAY &#8211; Temperature reconstruction combining boreholes thermometry and ice-cores with Bayesian hierarchical modelling &#8211; Global warming vs. natural variability in DML, Antarctica&#8221; (duration: 2026-2028)Research project within the DFG SPP 1158 &#8220;Antarctic Research&#8221; (joint project with Thomas Laepple,<\/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-22","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/22","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=22"}],"version-history":[{"count":63,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/22\/revisions"}],"predecessor-version":[{"id":1572,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/pages\/22\/revisions\/1572"}],"wp:attachment":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=22"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}