{"id":31667,"date":"2025-11-25T17:06:26","date_gmt":"2025-11-25T16:06:26","guid":{"rendered":"http:\/\/&amp;p=26905"},"modified":"2025-11-25T17:06:26","modified_gmt":"2025-11-25T16:06:26","slug":"learning-the-physics-of-free-surface-fluid-flows-with-ai","status":"publish","type":"tribe_events","link":"https:\/\/www.dica.polimi.it\/en\/evento\/learning-the-physics-of-free-surface-fluid-flows-with-ai\/","title":{"rendered":"Learning the physics of free-surface fluid flows with AI"},"content":{"rendered":"<p>Il prossimo appuntamento con la serie di incontri\u00a0<strong>PhDTalks\u00a0<\/strong>si terr\u00e0\u00a0<strong>Mercoled\u00ec 3 dicembre\u00a0<\/strong>nell\u2019<strong>aula Grandori\u00a0<\/strong>(Edificio 4)<strong>, dalle 12:00 alle 13:00 CET<\/strong>.<\/p>\n<p><strong>PhDTalks<\/strong>\u00a0\u00e8 una serie di seminari e discussioni tra dottorandi. Gli eventi hanno lo scopo di fornire un luogo dove creare un\u00a0<strong>network<\/strong>\u00a0tra dottorandi ed entrare in contatto con i molti<strong>\u00a0ongoing projects<\/strong>\u00a0sviluppati nel nostro dipartimento.<\/p>\n<p>Lo speaker <strong>Federico Lanteri<\/strong>\u00a0condurr\u00e0 un seminario dal titolo\u00a0<strong>\u201c<span data-olk-copy-source=\"MessageBody\">Learning the physics of free-surface fluid flows with AI<\/span><\/strong><strong>\u201d<\/strong>.<strong>\u00a0<\/strong><\/p>\n<p>Al termine dell\u2019evento sar\u00e0 disponibile un piccolo rinfresco finanziato dal dipartimento.<\/p>\n<p>Sar\u00e0 possibile seguire la conferenza anche online al seguente\u00a0<a href=\"https:\/\/politecnicomilano.webex.com\/meet\/marco.davolio\" target=\"_blank\" rel=\"noopener\"><strong><em>link<\/em><\/strong><\/a>.<\/p>\n<p><strong>Abstract<br \/>\n<\/strong><span data-olk-copy-source=\"MessageBody\">The study of free-surface fluid flows is of significant interest across various research fields, including civil, aerospace, and biomedical engineering. Numerical simulations approximating Navier\u2013Stokes equations provide accurate descriptions of such phenomena but are often computationally expensive for real-world applications. In recent years, deep learning has emerged as a promising approach for modeling complex physical systems, offering data-driven alternatives to traditional solvers. However, applying these techniques to free-surface flows is particularly challenging compared to standard fluid dynamics problems on fixed geometries, as it requires neural network architectures capable of handling unstructured and dynamically evolving domains. By carefully designing such architectures and training them on datasets generated from high-fidelity numerical simulations, it is possible to learn the complex underlying physics governing free-surface dynamics, effectively building surrogate models that reproduce accurate solutions at a fraction of the computational cost. This approach opens new perspectives for real-time analysis, optimization, and control in engineering applications involving free-surface flows.<\/span><\/p>\n<p><strong>Speaker\u2019s bio<br \/>\n<\/strong>Federico is a PhD candidate in Structural, Seismic, and Geotechnical Engineering (40th cycle). He obtained a master degree in Mathematical Engineering in 2024 and he is now in the second year of his PhD. His work focuses on developing machine-learning methods to enhance and speed up simulations of free-surface fluid flows, in particular cases involving complex materials.<\/p>\n<div class=\"x_elementToProof\" data-ogsc=\"rgb(0, 0, 0)\">In his free time, Federico enjoys playing the saxophone, hiking, running and playing tennis<\/div>","protected":false},"excerpt":{"rendered":"<p>Il prossimo appuntamento con la serie di incontri\u00a0PhDTalks\u00a0si terr\u00e0\u00a0Mercoled\u00ec 3 dicembre\u00a0nell\u2019aula Grandori\u00a0(Edificio 4), dalle 12:00 alle 13:00 CET. PhDTalks\u00a0\u00e8 una serie di seminari e discussioni tra dottorandi. 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