BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Copenhagen BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261025T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260924T130034Z UID:A11EC15E-770A-4D99-B2BF-B27D16360E3D DTSTART;TZID=Europe/Copenhagen:20260924T140000 DTEND;TZID=Europe/Copenhagen:20260924T150000 DESCRIPTION:In recent years\, machine learning has become an essential tool for the design and analysis of complex RF and microwave systems. This tal k highlights recent advances in machine learning–based methodologies\, d eveloped by considering the characteristics of the system under study and how its physical properties are represented in the training data (physic-i nformed approach). Both neural network–based techniques and data-efficie nt methods\, particularly those relying on Gaussian processes\, will be di scussed. Their key characteristics will be outlined\, along with their app lication to challenging electromagnetic compatibility (EMC) problems.\n\nS peaker(s): Domenico\, \n\nVirtual: https://events.vtools.ieee.org/m/572073 LOCATION:Virtual: https://events.vtools.ieee.org/m/572073 ORGANIZER:pda@energy.aau.dk SEQUENCE:12 SUMMARY:Physics-Informed Machine Learning for EMC: Techniques and Applicati ons URL;VALUE=URI:https://events.vtools.ieee.org/m/572073 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\;"><span lang="EN-US">In recent years\, machine learning has become an essential tool for the design and analysis of complex RF and microwave sys tems. This talk highlights recent advances in machine learning&ndash\;base d methodologies\, developed by considering the characteristics of the syst em under study and how its physical properties are represented in the trai ning data (physic-informed approach). Both neural network&ndash\;based tec hniques and data-efficient methods\, particularly those relying on Gaussia n processes\, will be discussed. Their key characteristics will be outline d\, along with their application to challenging electromagnetic compatibil ity (EMC) problems.</span></p> END:VEVENT END:VCALENDAR