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DTSTART;TZID=America/New_York:20210621T120000
DTEND;TZID=America/New_York:20210621T123000
DTSTAMP:20260406T205525
CREATED:20210520T132454Z
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SUMMARY:PSOC Webinar: Manasvita Vashisth & Mengdi Tao
DESCRIPTION:Join Zoom Meeting: \nhttps://upenn.zoom.us/j/99334915941?pwd=eDRXV1lITDlySXFyRHUyUzdmRldoQT09 \nMeeting ID: 993 3491 5941 \nPasscode: 189247 \nPSOC@Penn Summer Webinars 2021 \nContact manu@seas.upenn.edu with any questions \nManasvita Vashisth 12:00-12:30 PM \nMengdi Tao 12:30-1:00 PM
URL:https://seasevents.nmsdev7.com/event/psoc-webinar-manasvita-vashisth/
LOCATION:https://upenn.zoom.us/j/96715197752
CATEGORIES:Seminar,Doctoral,Graduate,Student
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
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DTSTART;TZID=America/New_York:20210622T103000
DTEND;TZID=America/New_York:20210622T120000
DTSTAMP:20260406T205525
CREATED:20210614T173046Z
LAST-MODIFIED:20210614T173046Z
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SUMMARY:MEAM Seminar: "Dynamic Response of Resonant Metamaterials and Other Composites"
DESCRIPTION:Composite materials under dynamic loading exhibit interesting emergent phenomena. Most notably are phononic crystals and metamaterials which can possess frequency dependent and negative elastic moduli and density. In general\, the constitutive response is non-local in space and time and depends on both the strain and velocity. Additionally\, scattering and local resonance effects can lead to frequency band gaps where no waves may propagate. Advanced manufacturing techniques allow us to produce these composites with intricate microstructures. At the same time\, these processes may be costly and prototyping many designs physically may be cost prohibitive. It is then natural to turn towards modeling in order to more efficiently design and characterize such materials. These modelling efforts may include direct numerical simulations\, but even then this may be computationally infeasible for composites whose microstructure is much smaller than the macroscopic length scales in which it will be operating. In these cases\, we seek to describe the effective behavior of the material under static or dynamic loading. \nThe work discussed in this talk is situated towards modeling of resonant metamaterials and other composites under dynamic loading. Direct numerical simulations are used to explore the wave propagation behavior of simple and hierarchical resonant metamaterials made of soft polydimethylsiloxane rubber (PDMS) and removable steel insets. The role of several physical features on the transmission loss (TL) curve is assessed in detail numerically and compared to the experimental TL data. Beyond this\, we develop a novel dynamic homogenization framework using one- and two-point statistics that provide estimates of the dynamic response of composites with reduced computational cost.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-dynamic-response-of-resonant-metamaterials-and-other-composites/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
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DTSTART;TZID=America/New_York:20210623T163000
DTEND;TZID=America/New_York:20210623T180000
DTSTAMP:20260406T205525
CREATED:20210603T153304Z
LAST-MODIFIED:20210603T153304Z
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SUMMARY:ODEI Spotlight: Pride on Ice
DESCRIPTION:Pride on Ice\nWednesday\, June 23rd | 4:30-6:00 pm\nPenn Ice Rink (3130 Walnut St)\n\nBe part of Penn on Ice\, an interactive experience\, and join us for free skating (or skate watching)\, great music\, and loads of LGBTQ+ pride! Come in pride finery or as you are\, as we will have some fun swag to liven up the party. Graduate and undergraduate students\, staff\, faculty\, alum\, and their families are welcome! *PennOpen Passes will be checked\, and masks must be worn!
URL:https://seasevents.nmsdev7.com/event/odei-spotlight-pride-on-ice/
LOCATION:PA
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210624T160000
DTEND;TZID=America/New_York:20210624T170000
DTSTAMP:20260406T205525
CREATED:20210622T181843Z
LAST-MODIFIED:20210622T181843Z
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SUMMARY:Doctoral Dissertation Defense: "Machine Learning for Robot Motion Planning"
DESCRIPTION:Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover’s problem to more complicated kinodynamic planning problems\, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics\, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with tradeoffs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First\, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next\, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly\, automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems.\n\nEmail dtadros@seas.upenn.edu for Zoom link.
URL:https://seasevents.nmsdev7.com/event/dissertation-defense-machine-learning-for-robot-motion-planning/
LOCATION:PA
CATEGORIES:Dissertation or Thesis Defense
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