BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Silicon Valley Engineering Council - ECPv6.18.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Silicon Valley Engineering Council
X-ORIGINAL-URL:https://svec.org
X-WR-CALDESC:Events for Silicon Valley Engineering Council
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261029T160000
DTEND;TZID=America/Los_Angeles:20261029T173000
DTSTAMP:20261014T030304Z
CREATED:20261014T030304Z
LAST-MODIFIED:20261014T030304Z
UID:79020-1793289600-1793295000@svec.org
SUMMARY:Stanford MTT-S/APS DL Talk: Scientific Machine Learning for Electromagnetic Field Computations
DESCRIPTION:Abstract:\nA recent report by the US Department of Energy defines the area of scientific machine learning as\n“a core component of artificial intelligence (AI) and a computational technology that can be trained\, with\nscientific data\, to augment or automate human skills”\, which has “the potential to transform science and\nenergy research”. In this presentation\, we discuss the potential of scientific machine learning methods to\nproblems in computational electromagnetics starting from standard electromagnetic structure analysis\nand multi-physics modeling in the time-domain\, employing unsupervised\, supervised and reinforcement\nlearning.\nAn unsupervised learning strategy is based on a modified Physics-Informed Neural Network (PINN). PINNs\ndirectly integrate physical laws into their loss function\, so that the training process does not rely on the\ngeneration of training ground truth data from simulations. We introduce a physics-informed deep-\noperator-network (PI-DON) for Maxwell’s equations. We show applications of the PI-DON to the analysis\,\ndesign and uncertainty quantification study of finite and defect periodic structures\, including frequency\nselective surfaces and metasurfaces.\nMoreover\, we demonstrate the impact of machine learning on the computational modeling of radiowave\npropagation scenarios and reconfigurable intelligent surfaces\, using supervised learning. We build\nconvolutional neural network models that can process the geometry of indoor environments\, along with\nphysics-inspired parameters\, to rapidly estimate received signal strength (RSS) maps. Emphasis is placed\non the generalizability of these models\, which is their ability to "learn" the physics of radiowave\npropagation and produce accurate modeling predictions in new geometries well beyond those included in\ntheir training set.\nFinally\, the use of reinforcement learning for the optimization of reconfigurable intelligent surfaces will be\ndiscussed.\nSpeaker(s): Costas\nRoom: 204\, Bldg: David Packard Electrical Engineering Building\, 350 JANE STANFORD WAY\,  STANFORD\, California\, United States\, 94305
URL:https://svec.org/event/stanford-mtt-s-aps-dl-talk-scientific-machine-learning-for-electromagnetic-field-computations/
LOCATION:Room: 204\, Bldg: David Packard Electrical Engineering Building\, 350 JANE STANFORD WAY\,  STANFORD\, California\, United States\, 94305
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261029T170000
DTEND;TZID=America/Los_Angeles:20261029T180000
DTSTAMP:20261008T024807Z
CREATED:20261008T024807Z
LAST-MODIFIED:20261008T024807Z
UID:78998-1793293200-1793296800@svec.org
SUMMARY:Stanford IEEE Fireside Chat: Shang-Yi Chiang
DESCRIPTION:Stanford IEEE hosts a fireside chat with Shang-Yi Chiang\, former co-COO of TSMC and one of the most influential figures in modern semiconductor process technology. The conversation will cover the evolution of process technology\, the future of the foundry industry\, and advanced packaging\, followed by open audience Q&A.\nThursday\, October 29\, 5-6 PM. Free and open to Stanford students\, staff\, and faculty.\nDetails and RSVP: https://luma.com/gft0fwna\nRoom: 101\, Bldg: Packard Electrical Engineering Building\, 350 Jane Stanford Way\, Stanford\, California\, United States\, 94305
URL:https://svec.org/event/stanford-ieee-fireside-chat-shang-yi-chiang/
LOCATION:Room: 101\, Bldg: Packard Electrical Engineering Building\, 350 Jane Stanford Way\, Stanford\, California\, United States\, 94305
END:VEVENT
END:VCALENDAR