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DTSTART;TZID=America/New_York:20201207T120000
DTEND;TZID=America/New_York:20201207T130000
DTSTAMP:20260407T112916
CREATED:20200908T172016Z
LAST-MODIFIED:20200908T172016Z
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SUMMARY:PSOC Webinar: “Interplay of receptor tyrosine kinase activation at the cell surface and environmental mechanics” (Shalini Low-Nam)
DESCRIPTION:“Interplay of receptor tyrosine kinase activation at the cell surface and environmental mechanics” \nPhysical Sciences in Oncology Center PSOC@Penn \nFall 2020 Webinar Series Mondays @ Noon (EST) \nFor webinar links\, please contact manu@seas.upenn.edu
URL:https://seasevents.nmsdev7.com/event/psoc-webinar-interplay-of-receptor-tyrosine-kinase-activation-at-the-cell-surface-and-environmental-mechanics-shalini-low-nam/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201208T103000
DTEND;TZID=America/New_York:20201208T120000
DTSTAMP:20260407T112916
CREATED:20201124T222847Z
LAST-MODIFIED:20201124T222847Z
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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
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201209T150000
DTEND;TZID=America/New_York:20201209T160000
DTSTAMP:20260407T112916
CREATED:20200917T001220Z
LAST-MODIFIED:20200917T001220Z
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SUMMARY:CBE Seminar: "Bio-inspired Pathways to Manipulating Architecture and Mechanics in Polymeric Materials"
DESCRIPTION:Abstract\n \nFiber constructs are prevalent in natural systems\, from collagen fiber networks in tendon to tough\, spider silk fibers. Recent innovations in multilayer co-extrusion technology have translated to the fabrication of melt-extruded fiber-reinforced composites\, reminiscent of the nanoscale features of the Brown Recluse Spider. Distinct advantages of this modular approach over other traditional techniques include scalability\, environmentally-friendly conditions\, and the ability to obtain cross-sectional dimensions on the nanoscale. Here\, we describe the mechanics and structural features of biologically-relevant\, reinforced hydrogels via an in situ approach. This manufacturing strategy allows for strategic control of hydrogel architecture\, fiber (single component and blends) alignment and loading\, and compressive stability and stiffness. Promising results related to cell adherence and growth\, and controlled degradation rates\, are highlighted for these extruded hydrogel scaffolds. \nSupramolecular interactions may hold the key to the development of elastomers with a tailored elastic response and improved mechanics\, such as observed in the muscle protein titin and polychaete worm jaw. It is the dynamic nature of the supramolecular interaction that we have exploited in the design of tough supramolecular elastomers that superimpose covalent and non-covalent interactions to tailor tensile response. In this research\, concepts of interfacial control of self-assembly\, composition\, and dynamics as it relates to mechanical behavior are examined. Supramolecular blends\, nanocomposites and interpenetrating networks have been investigated to achieve gradient mechanics\, shape memory response\, and bilayer actuation. These systems show promise in smart coating applications and for the development of functional polymer blends.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-bio-inspired-pathways-to-manipulating-architecture-and-mechanics-in-polymeric-materials/
LOCATION:Zoom – Email CBE for link
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201210T100000
DTEND;TZID=America/New_York:20201210T120000
DTSTAMP:20260407T112916
CREATED:20201204T190653Z
LAST-MODIFIED:20201204T190653Z
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SUMMARY:CBE PhD Dissertation Defense  | Integrin crosstalk in the upstream migration of CD4+ T lymphocytes
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/cbe-phd-dissertation-defense-integrin-crosstalk-in-the-upstream-migration-of-cd4-t-lymphocytes/
LOCATION:Zoom – Email CBE for link
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201210T104500
DTEND;TZID=America/New_York:20201210T114500
DTSTAMP:20260407T112916
CREATED:20200828T161336Z
LAST-MODIFIED:20200828T161336Z
UID:10006460-1607597100-1607600700@seasevents.nmsdev7.com
SUMMARY:MSE Graduate Student Seminar
DESCRIPTION:
URL:https://seasevents.nmsdev7.com/event/mse-graduate-student-seminar/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201210T110000
DTEND;TZID=America/New_York:20201210T120000
DTSTAMP:20260407T112916
CREATED:20201118T214105Z
LAST-MODIFIED:20201118T214105Z
UID:10006548-1607598000-1607601600@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "The Role of Explicit Regularization in Overparameterized Neural Networks"
DESCRIPTION:Overparameterized neural networks have proved to be remarkably successful in many complex tasks such as image classification and deep reinforcement learning. In this talk\, we will consider the role of explicit regularization in training overparameterized neural networks. Specifically\, we consider ReLU networks and show that the landscape of commonly used regularized loss functions have the property that every local minimum has good memorization and regularization performance. Joint work with Shiyu Liang and Ruoyu Sun.
URL:https://seasevents.nmsdev7.com/event/ese-seminar-the-role-of-explicit-regularization-in-overparameterized-neural-networks/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
CATEGORIES:Seminar,Faculty,Colloquium,Graduate,Undergraduate
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
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