BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.19//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20200113T100000
DTEND;TZID=America/New_York:20200113T160000
DTSTAMP:20260408T073732
CREATED:20191205T161642Z
LAST-MODIFIED:20191205T161642Z
UID:10006342-1578909600-1578931200@seasevents.nmsdev7.com
SUMMARY:REACT@Penn 2020: Global Pathways to enable Innovative Materials Solutions for Urban Challenges
DESCRIPTION:How can science and technology help cities solve their problems?  Are there common urban air\, water and energy challenges for science to tackle? \n\n\n\n\n10:00 am\nUrban Challenges with (potential) Materials Solutions\nModel cities of Grenoble\, France\, Philadelphia\, USA and Seoul\, Korea\n\n\n1:30 pm\nAdoption and Adaptation of New Technologies for Urban Challenges\nJoshua Sperling\, National Renewable Energy Laboratory\n\n\n2:30 pm\nFacilitating New Technology-based Solutions for Urban Challenges\nPanel discussion that includes experts from the Water Center at Penn\, City of Philadelphia’s Department of Planning and Development and Office of Transportation\, Infrastructure & Sustainability\n\n\n\n\n\nhttps://react.seas.upenn.edu/event/react-at-penn-2020/
URL:https://seasevents.nmsdev7.com/event/reactpenn-2020-global-pathways-to-enable-innovative-materials-solutions-for-urban-challenges/
LOCATION:Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200114T103000
DTEND;TZID=America/New_York:20200114T120000
DTSTAMP:20260408T073732
CREATED:20200108T205330Z
LAST-MODIFIED:20200108T205330Z
UID:10006360-1578997800-1579003200@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Confined Curved Shells and their Elaborate Conformations"
DESCRIPTION:Curved shells\, when confined\, can deform to a broad assortment of large scale shapes and smaller scale wrinkling and folding patterns quite unlike what produced by their flat counterparts. The intrinsic\, natural curvature of shells is the central element that allows for this rich and very interesting morphological landscape\, but it is also the source of geometric nonlinearities that renders an analytic treatment of non‐Euclidean shells\, even under small load\, very difficult. In this talk we examine some snapshots of this morphological landscape. Inspired by the natural folding and unfolding of polled grains\, we use theory\, simulations and experiments to explore the large scale deformation of a confined thin spherical shell with an opening. We then proceed to investigate the surface topography of shallow doubly curved shells resting on a fluid substrate. The frustration due to the competing geometry of the flat substrate and the curved shell produces a wealth of highly reproducible and ordered\, or random and disordered patterns. From these examples\, we see Gaussian curvature emerging as a powerful tool that can generate complex patterns.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-confined-curved-shells-and-their-elaborate-conformations/
LOCATION:Glandt Forum\, Singh Center for Nanotechnology\, 3205 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200115T150000
DTEND;TZID=America/New_York:20200115T160000
DTSTAMP:20260408T073732
CREATED:20200110T184725Z
LAST-MODIFIED:20200110T184725Z
UID:10006379-1579100400-1579104000@seasevents.nmsdev7.com
SUMMARY:MSE Materials in Practice Seminar: "So You're an Engineer...Now What?"
DESCRIPTION:Materials in Practice is a seminar series where technologists who are using materials science to shape the world we live in share their secret to doing this. We hope that anyone who wants to apply the research they are doing to solve real world problems will be able to attend this Seminar Series.
URL:https://seasevents.nmsdev7.com/event/mse-materials-in-practice-seminar-so-youre-an-engineer-now-what/
LOCATION:LRSM Reading Room\, 3231 Walnut St.\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200116T104500
DTEND;TZID=America/New_York:20200116T114500
DTSTAMP:20260408T073732
CREATED:20200102T182754Z
LAST-MODIFIED:20200102T182754Z
UID:10006359-1579171500-1579175100@seasevents.nmsdev7.com
SUMMARY:MSE Grace Hopper Lecture: "Artificial Intelligence for Generating Materials Science Knowledge"
DESCRIPTION:The process of scientific inquiry involves observing a signal (data) and interpreting it to generate information (knowledge). For example\, in electron microscopy the signal may be a diffraction pattern from which information on crystal orientation may be deduced by applying diffraction theory. Science advances both through improvements in gathering data and in techniques for extracting knowledge from it. Artificial intelligence (AI) – a broad term comprising data science\, machine learning (ML)\, neural network computing\, computer vision\, and other technologies – opens new avenues for extracting information from high-dimensional materials data. In that sense\, AI offers the possibility to advance materials science in the same way as a new imaging modality or a new theoretical model. The applications of AI in materials science cut a broad swath\, from large\, labelled data sets the fit naturally in the Big Data paradigm to small\, sparse\, multimodal data sets that test the limits of cutting-edge AI. \nThis presentation will focus on AI applications in the context of image-based data\, including both visual features and composition\, processing\, or properties metadata. Computer vision (CV) representations are developed to numerically encode the visual information contained in images. ML tools are then selected based on the characteristics of the data set and the desired outcome. For example\, a large\, homogeneous data set of steel inclusions is best suited to a Deep Learning approach involving a purpose-built convolutional neural network. In contrast\, a random-forest method can find significant trends in a small\, multi-modal data set that includes microstructural\, crystallographic\, and micromechanical data. Complex image segmentation leverages a convolution neural network that has been trained using images very different from those it is applied to. These case studies will motivate a discussion of AI method selection based on data set characteristics and desired outcomes. The ultimate goal is to develop AI as a new tool for information extraction and knowledge generation in materials science.
URL:https://seasevents.nmsdev7.com/event/mse-grace-hopper-lecture-artificial-intelligence-for-generating-materials-science-knowledge/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
END:VCALENDAR