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BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Penn Engineering Events - ECPv6.18.0//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:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD 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 END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20201208T103000 DTEND;TZID=America/New_York:20201208T120000 DTSTAMP:20201124T222847Z CREATED:20201124T222847Z LAST-MODIFIED:20201124T222847Z UID:3705-1607423400-1607428800@seasevents.nmsdev7.com SUMMARY:MEAM Seminar: "Bridging Physical Models and Observational Data with Physics-informed Deep Learning" DESCRIPTION:Physical models of many natural and engineered systems are\, at best\, only partially known; a common setting under which classical analytical or computational tools inevitably face challenges and introduce many sources of uncertainty. Therefore\, observational data plays a crucial role\, yet our ability to collect them far outpaces our ability to sensibly assimilate it\, let alone understand it. Despite their towering empirical success\, machine learning approaches are not currently able to extract interpretable information and knowledge from this data deluge. Moreover\, purely data-driven methods may fit observations very well\, but predictions may be physically inconsistent or implausible\, due to extrapolation or observational biases\, for example. In this talk we will discuss the foundations of a new family of machine learning methods coined as physics-informed neural networks\, that aim to seamlessly bridge this gap by synthesizing incomplete physics-based models with imperfect observational data. Specifically\, we will illustrate the mechanisms by which deep neural networks can be constrained to respect fundamental laws of physics\, but also highlight certain pathologies and limitations that arise during this process. Strikingly\, some of the latter can be addressed by exploring connections to classical methods in numerical analysis and optimization\, opening the path to designing more principled algorithms and deep learning architectures that do not simply rely on guesswork. Finally\, we will demonstrate the power of these methods across a range of diverse engineering applications\, including problems in design optimization\, heat transfer\, wave propagation\, cardiovascular fluid mechanics\, and modeling of COVID-19 spread dynamics. URL:https://seasevents.nmsdev7.com/event/meam-seminar-bridging-physical-models-and-observational-data-with-physics-informed-deep-learning/ 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