BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210406T103000
DTEND;TZID=America/New_York:20210406T120000
DTSTAMP:20260407T002558
CREATED:20210209T172446Z
LAST-MODIFIED:20210209T172446Z
UID:4168-1617705000-1617710400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Nonintrusive Reduced Order Models Using Physics Informed Neural Networks"
DESCRIPTION:The development of reduced order models for complex applications\, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation\, remains a very active research area. Applications are found in problems which require many evaluations\, sampled over a potentially large parameter space\, such as in optimization\, control\, uncertainty quantification\, and in applications where a near real-time response is needed. \nHowever\, many challenges remain unresolved to secure the flexibility\, robustness\, and efficiency needed for general large-scale applications\, in particular for nonlinear and/or time-dependent problems.\nAfter giving a brief general introduction to projection based reduced order models\, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications. \nIn the second part of the talk\, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model. \nTime permitting\, we finally discuss the use of reduced order models in the context of prediction\, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the Mori-Zwansig formulation for time-dependent problems\, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model. \nNB: This announcement has been updated with a new title and abstract.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-structure-preserving-reduced-order-models/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
END:VCALENDAR