{"id":1852,"date":"2026-02-27T11:10:49","date_gmt":"2026-02-27T10:10:49","guid":{"rendered":"https:\/\/www.hsu-hh.de\/hpccp\/?post_type=tribe_events&p=1852"},"modified":"2026-10-28T11:32:27","modified_gmt":"2026-10-28T10:32:27","slug":"seminar-series-hpc-cafe-computation-data-10","status":"publish","type":"tribe_events","link":"https:\/\/www.hsu-hh.de\/hpccp\/event\/seminar-series-hpc-cafe-computation-data-10\/","title":{"rendered":"Seminar Series & HPC Caf\u00e9: Computation & Data"},"content":{"rendered":"
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Seminar Series<\/strong> @16:00-17:00 (hybrid) & HPC Caf\u00e9<\/strong> @17:00-18:00 (on-site)<\/p>\n\n\n\n

4:00-5:00 (hybrid): Scientific Talk<\/em><\/strong> by Zhen Li<\/strong>[1] (Clemson University)
Neural Operator Learning Applied to Multiscale Complex Fluids
<\/u><\/strong>Intrinsic multiscale features in various physical systems stem from hierarchical structures that span a broad spectrum of temporal and spatial scales beyond the reach of any single simulation method, which have been recognized as significant challenges in multiscale engineering, especially for complex fluids and multifunctional materials. Recent advancements in deep learning, particularly in deep neural networks, have shown remarkable success in different scientific research fields. In this talk, I will introduce two deep-learning-enabled strategies for addressing such challenges. First,  I will introduce a deep neural operator learning approach for predicting multirate bubble growth dynamics across scales, bridging nanoscale mechanisms to macroscopic continuum behavior. Subsequently, I will describe a combined neural operator learning and physics-informed symbolic regression to discover interpretable mathematical models directly from computational data, demonstrated on the spreading dynamics of highly viscous liquids. Also, I will briefly introduce some of our ongoing efforts that extend neural operator learning to accelerate the design of composite materials and to optimize their manufacturing process. <\/p>\n\n\n\n

5:00-6:00 (on-site): HPC Caf\u00e9<\/em><\/strong> <\/strong>with Ruben Horn <\/strong>(HSU<\/abbr>\/UniBw H) as an expert for Energy Efficiency<\/u><\/strong><\/p>\n\n\n\n

What is the HPC Caf\u00e9?<\/strong>
At the HPC Caf\u00e9, we offer you practical support in aspects of High Performance Computing and related fields<\/strong> \u2013 using a peer-to-peer approach. This means you can bring your specific questions<\/strong>, whether they\u2019re about software, code optimization, or parallelization as well as specific questions regarding ANSYS, GNU, User Spack – you name it. We will help you directly or connect you with other researchers facing similar challenges.<\/p>\n\n\n\n

Our offer is open to all user groups<\/strong> \u2013 whether you\u2019re a beginner or an advanced user. Even if you are reading about HPC for the first time and you are interested in more information, the HPC Caf\u00e9 is just the right space for you to connect with people working in this field. <\/p>\n\n\n\n

Stop by, bring your challenges, and let\u2019s find solutions together!<\/p>\n\n\n

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H1, room 202<\/h3>\n\t<\/div>\n\n\t\n\t\t\t\t\t\t
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