{"id":30644,"date":"2026-05-01T14:21:47","date_gmt":"2026-05-01T12:21:47","guid":{"rendered":"https:\/\/sano.science\/?p=30644"},"modified":"2026-05-08T10:48:04","modified_gmt":"2026-05-08T08:48:04","slug":"sano-representatives-at-the-high-performance-computing-users","status":"publish","type":"post","link":"https:\/\/sano.science\/sano-representatives-at-the-high-performance-computing-users\/","title":{"rendered":"Sano Representatives at the High Performance Computing Users"},"content":{"rendered":"\n

Sano Centre for Computational Medicine had a strong presence at this year\u2019s High Performance Computing Users HPC Users\u2019 Conference – KUKDM 2026, where our researchers presented five posters highlighting current work in computational medicine, bioinformatics, and large-scale data analysis.<\/p>\n\n\n\n

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Sano Poster Presentations<\/h2>\n\n\n\n

The scientific posters presented by the Sano team reflected the breadth of the centre\u2019s research in high-performance computing applications for medicine and biological sciences.<\/p>\n\n\n\n

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‘Benchmarking a Heterogeneous Metagenomics Pipeline on Ares and Helios Supercomputers’<\/p>\n\n\n\n

Authors: Piotr Kica<\/a>, Kinga Zieli\u0144ska, Micha\u0142 Orzechowski, Karol Zaj\u0105c<\/a>,Tomasz Ko\u015bci\u00f3\u0142ek<\/a>, Maciej Malawski<\/a><\/p>\n\n\n\n

The team benchmarked a heterogeneous metagenomics pipeline on Poland\u2019s Ares and Helios supercomputers, demonstrating the capabilities of national HPC infrastructure for genomic data analysis.<\/p>\n\n\n\n

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HPC in Global Quantification and Personalization of Regulated Cardiovascular Model \u2013 a Cohort Study<\/strong>‘\u00a0<\/p>\n\n\n\n

Authors: Karolina Tla\u0142ka<\/a>, Ian Halliday, Andrew Narracott<\/a>, Maciej Malawski<\/a>
The presentation demonstrated the application of high-performance computing to personalized cardiovascular modeling across patient cohorts, enabling global quantification and individualization of regulated cardiovascular models.<\/p>\n<\/div>\n<\/div>\n\n\n

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Automating CFD Workflows on HPC with PyFluent: from Batch Simulations to Large-Scale Study<\/strong>‘<\/p>\n\n\n\n

Autors: Karol Zaj\u0105c<\/a>,\u00a0Magdalena Otta<\/a>, Maciej Malawski<\/a>, Andrew Narracott<\/p>\n\n\n\n

This poster presented an approach for automating CFD workflows using PyFluent and HPC systems. The proposed solution enables scalable and reproducible simulation campaigns through Python-based workflow automation. By leveraging HPC resources, the workflow significantly reduces computation time and supports large-scale studies involving thousands of simulations. The work also demonstrates the potential of automated CFD pipelines for scalable and complex UQ and SA studies.<\/p>\n<\/div>\n<\/div>\n\n\n

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‘PubDB: an Institutional Publication Database for KPI Monitoring and Visibility<\/strong>‘<\/p>\n\n\n\n

Authors: Taras Zhyhulin<\/a>, Adam Nowak<\/a>, Jan Meizner<\/a>, Piotr Nowakowski<\/a><\/p>\n\n\n\n

This work presented an institutional publication database designed to monitor and enhance the visibility of scientific output.<\/p>\n<\/div>\n<\/div>\n\n\n

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‘Evaluating the Stability of Deep Neural Networks for Brain Microstructural Parameter Estimation under Varying Diffusion Gradient Schemes<\/strong>‘<\/p>\n\n\n\n

Authors: Dominika Ciupek<\/a>, Maciej Malawski<\/a>, Tomasz Pici\u0105k<\/p>\n\n\n\n

The study evaluated the stability of deep neural networks for estimating brain microstructural parameters across different diffusion gradient schemes.<\/p>\n<\/div>\n<\/div>\n\n\n

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‘High\u2011Throughput Statistical Shape Modelling of Venous Anatomy Using Containerised Deformetrica on Ares Supercomputer<\/strong>‘<\/p>\n\n\n\n

Autors: Magdalena Otta<\/a>, Piotr Kica<\/a>, Maciej Malawski<\/a><\/p>\n\n\n\n

This work presents containerised approaches to high-throughput statistical shape modelling of venous anatomy, leveraging the Ares supercomputer infrastructure for scalable and efficient analysis, particularly in the context of large cohorts of geometries prepared for downstream computational fluid dynamics (CFD) studies.<\/p>\n<\/div>\n<\/div>\n\n\n

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‘Using High Performance Computing for the Application of PINNs in Cardiovascular Models’<\/strong><\/p>\n\n\n\n

Authors: Elena G\u00f3mez del Pozo<\/a>, Karolina Tla\u0142ka<\/a>, Karol Zaj\u0105c<\/a>, Maciej Malawski<\/a><\/p>\n\n\n\n

The presentation explored the application of Physics-Informed Neural Networks (PINNs) in cardiovascular modeling using high-performance computing. Unlike classical neural networks, PINNs incorporate known physics as part of the loss term. In this project, several loss term combinations were evaluated — such as mass conservation and no-negative-volumes — studying which one is most effective for fitting cardiovascular models of different complexity.<\/p>\n<\/div>\n<\/div>\n\n\n

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Keynote Speakers<\/strong><\/p>\n\n\n\n

This strong poster presence was complemented by the participation of distinguished speakers closely connected to Sano and its partner network. Among the keynote lecturers was Dr Olav Zimmermann from the J\u00fclich Supercomputing Centre<\/strong> in Germany, a representative of one of Sano\u2019s partner institutions, who delivered the talk ‘The stubborn old protein folding problem in the era of ExaFlops, Qbits and AI’. Another keynote address was given by Dr Marian Bubak<\/a>, Scientific Affairs Director at Sano and affiliated with ACK Cyfronet AGH in Krak\u00f3w, who presented ‘Towards European Infrastructure for In Silico Medicine’.<\/p>\n\n\n\n

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About KUKDM Conference<\/h2>\n\n\n\n

KUKDM itself has been organized by ACK Cyfronet AGH since 2008 as an annual meeting for researchers using high-performance computing systems and software provided by Cyfronet. Its thematic scope includes large-scale computing and simulations, new algorithms in computer science, tools and techniques for HPC systems, artificial intelligence methods, and the analysis of large datasets. The 2026 edition brought together representatives of science, business, and public administration, and featured guest lectures, 31 presentations, and 12 posters by users of the Polish PLGrid infrastructure coordinated by ACK Cyfronet AGH. In addition to its scientific sessions, the conference offers training opportunities and consultations with the PLGrid support team, making it not only a forum for presenting results but also a practical space for improving the use of supercomputing in research.<\/p>\n","protected":false},"excerpt":"HPC Users\u2019 Conference - KUKDM 2026","author":8,"featured_media":30649,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"editor_plus_post_options":"{}","editor_plus_copied_stylings":"{}","footnotes":""},"categories":[1],"tags":[],"class_list":["post-30644","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nSano Representatives at the High Performance Computing Users - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"description\" content=\"The Eighteenth HPC Users' Conference - KUKDM 2026\" \/>\n<meta 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class=\" eplus-wrapper\">Sano Centre for Computational Medicine had a strong presence at this year\u2019s High Performance Computing Users HPC Users\u2019 Conference - KUKDM 2026, where our researchers presented five posters highlighting current work in computational medicine, bioinformatics, and large-scale data analysis.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Sano Centre for Computational Medicine had a strong presence at this year\u2019s High Performance Computing Users HPC Users\u2019 Conference - KUKDM 2026, where our researchers presented five posters highlighting current work in computational medicine, bioinformatics, and large-scale data analysis.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-U0GLlk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-zCTzgd","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-sano-poster-presentations\">Sano Poster Presentations<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-sano-poster-presentations\">Sano Poster Presentations<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-p0lK2Y","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The scientific posters presented by the Sano team reflected the breadth of the centre\u2019s research in high-performance computing applications for medicine and biological sciences.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The scientific posters presented by the Sano team reflected the breadth of 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style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"width":"33.33%","epAnimationGeneratedClass":"edplus_anim-hTsoWh","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-w9XU95","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p id=\"h-\" class=\" eplus-wrapper\">'Benchmarking a Heterogeneous Metagenomics Pipeline on Ares and Helios Supercomputers'<\/p>\n","innerContent":["\n<p id=\"h-\" class=\" eplus-wrapper\">'Benchmarking a Heterogeneous Metagenomics Pipeline on Ares and Helios Supercomputers'<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Bniqkq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p id=\"h-benchmarking-a-heterogeneous-metagenomics-pipeline-on-ares-and-helios-supercomputers-authors-piotr-kica-kinga-zielinska-michal-orzechowski-karol-zajac-tomasz-kosciolek-maciej-malawski\" class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/piotr-kica\/\" type=\"people\" id=\"12104\">Piotr Kica<\/a>, Kinga Zieli\u0144ska, Micha\u0142 Orzechowski, <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>,<a href=\"https:\/\/sano.science\/people\/tomasz-kosciolek\/\" type=\"people\" id=\"12707\">Tomasz Ko\u015bci\u00f3\u0142ek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n","innerContent":["\n<p id=\"h-benchmarking-a-heterogeneous-metagenomics-pipeline-on-ares-and-helios-supercomputers-authors-piotr-kica-kinga-zielinska-michal-orzechowski-karol-zajac-tomasz-kosciolek-maciej-malawski\" class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/piotr-kica\/\" type=\"people\" id=\"12104\">Piotr Kica<\/a>, Kinga Zieli\u0144ska, Micha\u0142 Orzechowski, <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>,<a href=\"https:\/\/sano.science\/people\/tomasz-kosciolek\/\" type=\"people\" id=\"12707\">Tomasz Ko\u015bci\u00f3\u0142ek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-vPTvJX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The team benchmarked a heterogeneous metagenomics pipeline on Poland\u2019s Ares and Helios supercomputers, demonstrating the capabilities of national HPC infrastructure for genomic data analysis.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The team benchmarked a heterogeneous metagenomics pipeline on Poland\u2019s Ares and Helios supercomputers, demonstrating the capabilities of national HPC infrastructure for genomic data 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style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"verticalAlignment":"center","width":"33.33%","epAnimationGeneratedClass":"edplus_anim-hTsoWh","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-vPTvJX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>HPC in Global Quantification and Personalization of Regulated Cardiovascular Model \u2013 a Cohort Study<\/strong>' <\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>HPC in Global Quantification and Personalization of Regulated Cardiovascular Model \u2013 a Cohort Study<\/strong>' <\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-vPTvJX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/karolina-tlalka\/\" type=\"people\" id=\"12871\">Karolina Tla\u0142ka<\/a>, Ian Halliday, <a href=\"https:\/\/sano.science\/people\/andrew-narracott\/\" type=\"people\" id=\"12218\">Andrew Narracott<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><br>The presentation demonstrated the application of high-performance computing to personalized cardiovascular modeling across patient cohorts, enabling global quantification and individualization of regulated cardiovascular models.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/karolina-tlalka\/\" type=\"people\" id=\"12871\">Karolina Tla\u0142ka<\/a>, Ian Halliday, <a href=\"https:\/\/sano.science\/people\/andrew-narracott\/\" type=\"people\" id=\"12218\">Andrew Narracott<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><br>The presentation demonstrated the application of high-performance computing to personalized cardiovascular modeling across patient cohorts, enabling global quantification and individualization of regulated cardiovascular models.<\/p>\n"]}],"innerHTML":"\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">\n\n<\/div>\n","innerContent":["\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">",null,"\n\n",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper eplus-styles-uid-d98c6e\">\n\n<\/div>","innerContent":["\n<div class=\"wp-block-columns eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-U0GLlk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" 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class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/Automating-CFD-Workflows-on-HPC-with-PyFluent-from-Batch-Simulations-to-Large-Scale-Study_KUKDM_2026-723x1024.jpg\" alt=\"\" class=\"wp-image-30657\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/Automating-CFD-Workflows-on-HPC-with-PyFluent-from-Batch-Simulations-to-Large-Scale-Study_KUKDM_2026-723x1024.jpg\" alt=\"\" class=\"wp-image-30657\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"verticalAlignment":"center","width":"33.33%","epAnimationGeneratedClass":"edplus_anim-gSJvPw","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-tpGuBG","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Automating CFD Workflows on HPC with PyFluent: from Batch Simulations to Large-Scale Study<\/strong>'<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Automating CFD Workflows on HPC with PyFluent: from Batch Simulations to Large-Scale Study<\/strong>'<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Nj0slK","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Otta-scaled.jpg\" type=\"attachment\" id=\"27319\">Magdalena Otta<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a>, Andrew Narracott<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/12\/Otta-scaled.jpg\" type=\"attachment\" id=\"27319\">Magdalena Otta<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a>, Andrew Narracott<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-tpGuBG","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This poster presented an approach for automating CFD workflows using PyFluent and HPC systems. The proposed solution enables scalable and reproducible simulation campaigns through Python-based workflow automation. By leveraging HPC resources, the workflow significantly reduces computation time and supports large-scale studies involving thousands of simulations. The work also demonstrates the potential of automated CFD pipelines for scalable and complex UQ and SA studies.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This poster presented an approach for automating CFD workflows using PyFluent and HPC systems. The proposed solution enables scalable and reproducible simulation campaigns through Python-based workflow automation. By leveraging HPC resources, the workflow significantly reduces computation time and supports large-scale studies involving thousands of simulations. 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class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/PubDB-an-Institutional-Publication-Database-for-KPI-Monitoring-and-Visibility_KUKDM2026-724x1024.jpg\" alt=\"\" class=\"wp-image-30653\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/PubDB-an-Institutional-Publication-Database-for-KPI-Monitoring-and-Visibility_KUKDM2026-724x1024.jpg\" alt=\"\" class=\"wp-image-30653\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\" 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href=\"https:\/\/sano.science\/people\/adam-nowak\/\" type=\"people\" id=\"12093\">Adam Nowak<\/a>, <a href=\"https:\/\/sano.science\/people\/jan-meizner\/\" type=\"people\" id=\"12015\">Jan Meizner<\/a>, <a href=\"https:\/\/sano.science\/people\/piotr-nowakowski\/\" type=\"people\" id=\"12077\">Piotr Nowakowski<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/taras-zhyhulin\/\" type=\"people\" id=\"12106\">Taras Zhyhulin<\/a>, <a href=\"https:\/\/sano.science\/people\/adam-nowak\/\" type=\"people\" id=\"12093\">Adam Nowak<\/a>, <a href=\"https:\/\/sano.science\/people\/jan-meizner\/\" type=\"people\" id=\"12015\">Jan Meizner<\/a>, <a href=\"https:\/\/sano.science\/people\/piotr-nowakowski\/\" type=\"people\" id=\"12077\">Piotr Nowakowski<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-H53EQq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" 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style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"verticalAlignment":"center","width":"33.33%","epAnimationGeneratedClass":"edplus_anim-91Npc6","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-RS1DoD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">'<strong>Evaluating the Stability of Deep Neural Networks for Brain Microstructural Parameter Estimation under Varying Diffusion Gradient Schemes<\/strong>'<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">'<strong>Evaluating the Stability of Deep Neural Networks for Brain Microstructural Parameter Estimation under Varying Diffusion Gradient Schemes<\/strong>'<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-jchoWJ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/dominika-ciupek\/\" type=\"people\" id=\"12012\">Dominika Ciupek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a>, Tomasz Pici\u0105k<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/dominika-ciupek\/\" type=\"people\" id=\"12012\">Dominika Ciupek<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a>, Tomasz Pici\u0105k<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-RS1DoD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The study evaluated the stability of deep neural networks for estimating brain microstructural parameters across different diffusion gradient schemes.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The study evaluated the stability of deep neural networks for estimating brain microstructural parameters across different diffusion gradient schemes.<\/p>\n"]}],"innerHTML":"\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">\n\n\n\n<\/div>\n","innerContent":["\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">",null,"\n\n",null,"\n\n",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper eplus-styles-uid-512356\">\n\n<\/div>","innerContent":["\n<div class=\"wp-block-columns eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-XRCl0l","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer 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class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/High\u2011Throughput-Statistical-Shape-Modelling-of-Venous-Anatomy-Using-Containerised-Deformetrica-on-Ares-Supercomputer_KUKDM2026-719x1024.jpg\" alt=\"\" class=\"wp-image-30655\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/High\u2011Throughput-Statistical-Shape-Modelling-of-Venous-Anatomy-Using-Containerised-Deformetrica-on-Ares-Supercomputer_KUKDM2026-719x1024.jpg\" alt=\"\" class=\"wp-image-30655\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"verticalAlignment":"center","width":"33.33%","epAnimationGeneratedClass":"edplus_anim-D3A9Pk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fmMfPm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">'<strong>High\u2011Throughput Statistical Shape Modelling of Venous Anatomy Using Containerised Deformetrica on Ares Supercomputer<\/strong>'<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">'<strong>High\u2011Throughput Statistical Shape Modelling of Venous Anatomy Using Containerised Deformetrica on Ares Supercomputer<\/strong>'<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fmMfPm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/people\/magdalena-otta\/\" type=\"people\" id=\"12028\">Magdalena Otta<\/a>, <a href=\"https:\/\/sano.science\/people\/piotr-kica\/\" type=\"people\" id=\"12104\">Piotr Kica<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/people\/magdalena-otta\/\" type=\"people\" id=\"12028\">Magdalena Otta<\/a>, <a href=\"https:\/\/sano.science\/people\/piotr-kica\/\" type=\"people\" id=\"12104\">Piotr Kica<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-fmMfPm","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This work presents containerised approaches to high-throughput statistical shape modelling of venous anatomy, leveraging the Ares supercomputer infrastructure for scalable and efficient analysis, particularly in the context of large cohorts of geometries prepared for downstream computational fluid dynamics (CFD) studies.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This work presents containerised approaches to high-throughput statistical shape modelling of venous anatomy, leveraging the Ares supercomputer infrastructure for scalable and efficient analysis, particularly in the context of large cohorts of geometries prepared for downstream computational fluid dynamics (CFD) studies.<\/p>\n"]}],"innerHTML":"\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">\n\n\n\n<\/div>\n","innerContent":["\n<div class=\"wp-block-column is-vertically-aligned-center eplus-wrapper\" style=\"flex-basis:33.33%\">",null,"\n\n",null,"\n\n",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper 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class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/Using-High-Performance-Computing-for-the-Application-of-PINNs-in-Cardiovascular-Models-Poster-KUKDM_2026-723x1024.jpg\" alt=\"\" class=\"wp-image-30658\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/Using-High-Performance-Computing-for-the-Application-of-PINNs-in-Cardiovascular-Models-Poster-KUKDM_2026-723x1024.jpg\" alt=\"\" class=\"wp-image-30658\"\/><\/figure>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\"><\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:66.66%\">",null,"<\/div>\n"]},{"blockName":"core\/column","attrs":{"width":"33.33%","epAnimationGeneratedClass":"edplus_anim-vZbt4s","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-aj4TWJ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>'Using High Performance Computing for the Application of PINNs in Cardiovascular Models'<\/strong><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>'Using High Performance Computing for the Application of PINNs in Cardiovascular Models'<\/strong><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-9RJ2Y2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/elena-gomez-del-pozo\/\" type=\"people\" id=\"27069\">Elena G\u00f3mez del Pozo<\/a>, <a href=\"https:\/\/sano.science\/people\/karolina-tlalka\/\" type=\"people\" id=\"12871\">Karolina Tla\u0142ka<\/a>, <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Authors: <a href=\"https:\/\/sano.science\/people\/elena-gomez-del-pozo\/\" type=\"people\" id=\"27069\">Elena G\u00f3mez del Pozo<\/a>, <a href=\"https:\/\/sano.science\/people\/karolina-tlalka\/\" type=\"people\" id=\"12871\">Karolina Tla\u0142ka<\/a>, <a href=\"https:\/\/sano.science\/people\/karol-zajac\/\" type=\"people\" id=\"12098\">Karol Zaj\u0105c<\/a>, <a href=\"https:\/\/sano.science\/people\/maciej-malawski\/\" type=\"people\" id=\"531\">Maciej Malawski<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-aj4TWJ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The presentation explored the application of Physics-Informed Neural Networks (PINNs) in cardiovascular modeling using high-performance computing. Unlike classical neural networks, PINNs incorporate known physics as part of the loss term. In this project, several loss term combinations were evaluated -- such as mass conservation and no-negative-volumes -- studying which one is most effective for fitting cardiovascular models of different complexity.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The presentation explored the application of Physics-Informed Neural Networks (PINNs) in cardiovascular modeling using high-performance computing. Unlike classical neural networks, PINNs incorporate known physics as part of the loss term. In this project, several loss term combinations were evaluated -- such as mass conservation and no-negative-volumes -- studying which one is most effective for fitting cardiovascular models of different complexity.<\/p>\n"]}],"innerHTML":"\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:33.33%\">\n\n\n\n<\/div>\n","innerContent":["\n<div class=\"wp-block-column eplus-wrapper\" style=\"flex-basis:33.33%\">",null,"\n\n",null,"\n\n",null,"<\/div>\n"]}],"innerHTML":"<div class=\"wp-block-columns eplus-wrapper eplus-styles-uid-b03039\">\n\n<\/div>","innerContent":["\n<div class=\"wp-block-columns eplus-wrapper\">",null,"\n\n",null,"<\/div>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-XRCl0l","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-9RJ2Y2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keynote Speakers<\/strong><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keynote Speakers<\/strong><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-9RJ2Y2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This strong poster presence was complemented by the participation of distinguished speakers closely connected to Sano and its partner network. Among the keynote lecturers was Dr <strong>Olav Zimmermann from the J\u00fclich Supercomputing Centre<\/strong> in Germany, a representative of one of Sano\u2019s partner institutions, who delivered the talk 'The stubborn old protein folding problem in the era of ExaFlops, Qbits and AI'. Another keynote address was given by Dr <a href=\"https:\/\/sano.science\/people\/marian-bubak\/\" type=\"people\" id=\"534\">Marian Bubak<\/a>, Scientific Affairs Director at Sano and affiliated with ACK Cyfronet AGH in Krak\u00f3w, who presented 'Towards European Infrastructure for In Silico Medicine'.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This strong poster presence was complemented by the participation of distinguished speakers closely connected to Sano and its partner network. Among the keynote lecturers was Dr <strong>Olav Zimmermann from the J\u00fclich Supercomputing Centre<\/strong> in Germany, a representative of one of Sano\u2019s partner institutions, who delivered the talk 'The stubborn old protein folding problem in the era of ExaFlops, Qbits and AI'. Another keynote address was given by Dr <a href=\"https:\/\/sano.science\/people\/marian-bubak\/\" type=\"people\" id=\"534\">Marian Bubak<\/a>, Scientific Affairs Director at Sano and affiliated with ACK Cyfronet AGH in Krak\u00f3w, who presented 'Towards European Infrastructure for In Silico Medicine'.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-XRCl0l","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-eaORcv","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-kukdm-conference\">About KUKDM Conference<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-kukdm-conference\">About KUKDM Conference<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-tqy9NQ","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">KUKDM itself has been organized by ACK Cyfronet AGH since 2008 as an annual meeting for researchers using high-performance computing systems and software provided by Cyfronet. Its thematic scope includes large-scale computing and simulations, new algorithms in computer science, tools and techniques for HPC systems, artificial intelligence methods, and the analysis of large datasets. The 2026 edition brought together representatives of science, business, and public administration, and featured guest lectures, 31 presentations, and 12 posters by users of the Polish PLGrid infrastructure coordinated by ACK Cyfronet AGH. In addition to its scientific sessions, the conference offers training opportunities and consultations with the PLGrid support team, making it not only a forum for presenting results but also a practical space for improving the use of supercomputing in research.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">KUKDM itself has been organized by ACK Cyfronet AGH since 2008 as an annual meeting for researchers using high-performance computing systems and software provided by Cyfronet. Its thematic scope includes large-scale computing and simulations, new algorithms in computer science, tools and techniques for HPC systems, artificial intelligence methods, and the analysis of large datasets. The 2026 edition brought together representatives of science, business, and public administration, and featured guest lectures, 31 presentations, and 12 posters by users of the Polish PLGrid infrastructure coordinated by ACK Cyfronet AGH. In addition to its scientific sessions, the conference offers training opportunities and consultations with the PLGrid support team, making it not only a forum for presenting results but also a practical space for improving the use of supercomputing in research.<\/p>\n"]}],"meta_data":{"has_thumbnail_pattern":false,"share_on_social_media":{"has_social_media":false}},"featured_image":{"url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/05\/kukdm-2026-1024x544.jpg"},"main_category":{"name":"Uncategorized"},"prev_page":{"slug":"marian-bubak-appointed-to-the-krakow-entrepreneurship-council"},"next_page":{"slug":"sano-news-new-issue-out-now"},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30644","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/comments?post=30644"}],"version-history":[{"count":58,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30644\/revisions"}],"predecessor-version":[{"id":30712,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30644\/revisions\/30712"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/30649"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=30644"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/categories?post=30644"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/tags?post=30644"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}