{"id":14570,"date":"2025-04-27T21:32:35","date_gmt":"2025-04-27T19:32:35","guid":{"rendered":"https:\/\/www.eledia.org\/eledia-unitn\/?post_type=event&#038;p=14570"},"modified":"2025-04-27T21:32:35","modified_gmt":"2025-04-27T19:32:35","slug":"application-of-artificial-intelligence-and-deep-learning-to-computational-electromagnetics","status":"publish","type":"event","link":"https:\/\/www.eledia.org\/eledia-unitn\/sciencex_event\/application-of-artificial-intelligence-and-deep-learning-to-computational-electromagnetics\/","title":{"rendered":"Application of Artificial Intelligence and Deep Learning to Computational Electromagnetics"},"content":{"rendered":"\n<p>In recent years, research in artificial intelligence techniques comprising both optimization and machine learning methodologies has attracted much attention. In particular, with the help of big data technology, massively parallel computing, and fast optimization algorithms, deep learning has greatly improved the performance of many problems in speech and image research. In this short tutorial, the presenters will share some of their learnings in artificial intelligence and deep learning techniques and discuss the potential and feasibility of applying them in computational electromagnetics. After discussing the fundamentals of such paradigms, the presenters will explore the characteristics, feasibility, and challenges of these methods in computational electromagnetics through examples of solving electromagnetic forward modeling and inverse problems.<\/p>\n\n\n\n<p><strong>Speakers<\/strong><br>Maokun Li, Tsinghua University<br>Marco Salucci, University of Trento<\/p>\n\n\n\n<p><strong>When<\/strong>: Sunday, 13 July 2025, 09:00 &#8211; 12:00<\/p>\n\n\n\n<p><br><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In recent years, research in artificial intelligence techniques comprising both optimization and machine learning methodologies has attracted much attention. In particular, with the help of big data technology, massively parallel computing, and fast optimization algorithms, deep learning has greatly improved the performance of many problems in speech and image research. In this short tutorial, the [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":0,"template":"","event_type":[97],"class_list":["post","post-type","post-14570","event","type-event","status-publish","hentry","event_type-event-phdcourse",""],"acf":[],"_links":{"self":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/event\/14570","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/event"}],"about":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/types\/event"}],"author":[{"embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/users\/11"}],"wp:attachment":[{"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/media?parent=14570"}],"wp:term":[{"taxonomy":"event_type","embeddable":true,"href":"https:\/\/www.eledia.org\/eledia-unitn\/wp-json\/wp\/v2\/event_type?post=14570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}