BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260702T162400Z UID:671963F7-F2A5-4344-A2B3-C73B9FCFCA75 DTSTART;TZID=America/New_York:20260709T103000 DTEND;TZID=America/New_York:20260709T113000 DESCRIPTION:Computational Electromagnetics (CEM) is an interdisciplinary fi eld that combines principles from electrical engineering\, physics\, mathe matics\, and computer science to simulate and analyze electromagnetic phen omena. It serves as a cornerstone for the design and optimization of pract ical systems such as antennas\, microwave circuits\, radars\, satellites\, wireless communication devices\, and emerging applications in nanophotoni cs and biomedical imaging. The increasing complexity of modern systems— featuring irregular geometries\, inhomogeneous media\, and multiscale beha viors—necessitates robust and efficient modeling and simulation techniqu es.\n\nOver the past decades\, CEM has evolved to address challenges assoc iated with electrically large structures\, multiphysics environments\, and high-frequency regimes. Recent advancements in computing technologies—e specially GPUs and domain-specific hardware—have enabled researchers to solve problems with billions of unknowns\, while hybrid numerical schemes and parallel implementations ensure scalability and efficiency. Recent tre nds also include the use of machine learning-based surrogate models\, whic h are trained to approximate the behavior of computationally expensive sim ulations\, enabling faster predictions without compromising accuracy.\n\nT his lecture will begin by covering the theoretical foundations and numeric al implementations of classical CEM methods\, including the Method of Mome nts (MoM)\, Finite Element Method (FEM)\, Finite Difference (FD)\, and Fin ite Difference Time Domain (FDTD) method. Emphasis will be placed on their mathematical formulation\, discretization strategies\, and computational aspects. In the second part\, the focus will shift to advanced techniques used to tackle contemporary challenges in CEM\, such as hybrid methods\, d omain decomposition\, and large-scale parallel solvers. Current trends tha t are reshaping the future of the field— such as the integration of data -driven machine learning approaches into electromagnetic modeling workflow s—will be briefly highlighted. Real-world case studies will be presented to illustrate the practical applications of these methods in the simulati on of electromagnetic radiation and scattering problems.\n\nSpeaker(s): Pr ofessor Özgün\, \n\nRoom: PSE 7363 Faculty Hall\, Bldg: E7\, 200 Univer sity Ave W\, Waterloo\, Ontario\, Canada\, N2L 3G1 LOCATION:Room: PSE 7363 Faculty Hall\, Bldg: E7\, 200 University Ave W\, Wa terloo\, Ontario\, Canada\, N2L 3G1 ORGANIZER:amansoor@uwaterloo.ca SEQUENCE:57 SUMMARY:Computational Electromagnetics: From Basics to Mastery URL;VALUE=URI:https://events.vtools.ieee.org/m/565387 X-ALT-DESC:Description: <br /><p class="p1">Computational Electromagnetics (CEM) is an interdisciplinary field that combines principles from electric al&nbsp\;engineering\, physics\, mathematics\, and computer science to sim ulate and analyze electromagnetic&nbsp\;phenomena. It serves as a cornerst one for the design and optimization of practical systems such as&nbsp\;ant ennas\, microwave circuits\, radars\, satellites\, wireless communication devices\, and emerging&nbsp\;applications in nanophotonics and biomedical imaging. The increasing complexity of modern systems&mdash\;&nbsp\;featuri ng irregular geometries\, inhomogeneous media\, and multiscale behaviors&m dash\;necessitates robust and&nbsp\;efficient modeling and simulation tech niques.</p>\n<p class="p1">Over the past decades\, CEM has evolved to addr ess challenges associated with electrically large&nbsp\;structures\, multi physics environments\, and high-frequency regimes. Recent advancements in computing&nbsp\;technologies&mdash\;especially GPUs and domain-specific ha rdware&mdash\;have enabled researchers to solve&nbsp\;problems with billio ns of unknowns\, while hybrid numerical schemes and parallel implementatio ns ensure&nbsp\;scalability and efficiency. Recent trends also include the use of machine learning-based surrogate models\,&nbsp\;which are trained to approximate the behavior of computationally expensive simulations\, ena bling faster&nbsp\;predictions without compromising accuracy.</p>\n<p clas s="p1">This lecture will begin by covering the theoretical foundations and numerical implementations of classical&nbsp\;CEM methods\, including the Method of Moments (MoM)\, Finite Element Method (FEM)\, Finite Difference& nbsp\;(FD)\, and Finite Difference Time Domain (FDTD) method. Emphasis wil l be placed on their mathematical&nbsp\;formulation\, discretization strat egies\, and computational aspects. In the second part\, the focus will shi ft to&nbsp\;advanced techniques used to tackle contemporary challenges in CEM\, such as hybrid methods\, domain&nbsp\;decomposition\, and large-scal e parallel solvers. Current trends that are reshaping the future of the fi eld&mdash\;&nbsp\;such as the integration of data-driven machine learning approaches into electromagnetic modeling&nbsp\;workflows&mdash\;will be br iefly highlighted. Real-world case studies will be presented to illustrate the practical&nbsp\;applications of these methods in the simulation of el ectromagnetic radiation and scattering problems.</p> END:VEVENT END:VCALENDAR