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DTSTART;TZID=America/New_York:20230403T140000
DTEND;TZID=America/New_York:20230403T150000
DTSTAMP:20260404T153916
CREATED:20230327T151016Z
LAST-MODIFIED:20230327T151016Z
UID:10007532-1680530400-1680534000@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Wall-modeled Large-eddy Simulation of the Turbulent Boundary Layer with Mean-flow Three-dimensionality"
DESCRIPTION:The capability to predict high-Reynolds-number turbulent flows is essential for many natural and engineering flows such as external aerodynamics of wind turbines and aircraft wings\, flow over the hull of marine vehicles\, atmospheric boundary-layer flow over complex landscapes and cityscapes. However\, due to extreme disparity of scales present in high-Reynolds-number wall-bounded turbulent flows\, any attempt to simulate these flows directly on a computational grid without resorting to modeling of some sort results in prohibitively large computational cost. Wall-modeled large-eddy simulation (WMLES) show perhaps the most promise in being able to capture more of the relevant flow physics while keeping computational cost tractable in simulating these flows. There have been many novel wall models being developed during the last decades. However\, the applications of most of the models are limited to canonical two-dimensional turbulent flows such as the turbulent channel flow where non-equilibrium effects including pressure gradient and mean-flow three-dimensionality are missing. \nIn this talk\, I will present a comparative study of WMLES of a turbulent boundary layer with mean-flow three-dimensionality developing on the floor of a bent square duct which mimics the flow over the swept wing of the aircraft. The predictive capabilities of three widely used wall models\, namely\, a simple equilibrium stress model\, an integral nonequilibrium model\, and a PDE nonequilibrium model\, have been investigated. These models potentially span the complete spectrum of wall models with varying physical details and complexity. While the wall-stress magnitudes predicted by the three wall models are comparable\, the PDE nonequilibrium wall model produces a substantially more accurate prediction of the wall-stress direction\, followed by the integral nonequilibrium wall model. The wall-stress direction from the wall models is shown to have separable contributions from the equilibrium stress part and the integrated nonequilibrium effects\, where how the latter is modeled differs among the wall models. Budget analyses have been conducted to elucidate precise mechanisms by which the three wall models produce different predictions of the wall shear stress directions given almost identical inputs. The physical characteristics of the three-dimensional turbulent boundary layer including the generation mechanism of mean-flow three-dimensionality and the anisotropy of turbulence will also be discussed in the talk.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-wall-modeled-large-eddy-simulation-of-the-turbulent-boundary-layer-with-mean-flow-three-dimensionality/
LOCATION:Towne 309\, 220 S. 33rd 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;VALUE=DATE:20230404
DTEND;VALUE=DATE:20230406
DTSTAMP:20260404T153916
CREATED:20230123T171059Z
LAST-MODIFIED:20230123T171059Z
UID:10007438-1680566400-1680739199@seasevents.nmsdev7.com
SUMMARY:5th Annual Mechanobiology Symposium
DESCRIPTION:This two-day symposium is organized by the Center for Engineering MechanoBiology (CEMB) with support from the National Science Foundation. The theme will be MechanoImmunology and Epigenetics. \nThe CEMB is a Science and Technology Center funded by the National Science Foundation; our research focus is to advance the study of mechanical forces in molecules\, cells\, and tissues in plants and animals. The theme of this 5th symposium will be the role of forces on immunology and epigenetics. We are inviting leaders in these fields to join a relatively small group environment where new state-of-the-art research can be presented and discussed. \nThis meeting will be in-person at University of Pennsylvania in Philadelphia\, PA. While we encourage in-person attendance\, this meeting will also be streamed for registrants that cannot attend in person. \n  \nPlease register by March 1\, 2023 to gain access to the meeting. \nWe are currently accepting poster abstracts for our poster session. If you are interested in presenting a poster\, please submit a title and abstract (1500 character limit) HERE by March 1\, 2023. \nInvited speakers: \n\nAdelin Barbacci\, French National Centre for Scientific Research\, National Institute for Agriculture\, Food\, and Environment\nMarco Fritzsche\, Rosalind Franklin Institute\, Kennedy Institute for Rheumatology\, University of Oxford\nClaire Hivrov\, Institut Curie\nLance Kam\, Columbia University\nMatthew Lang\, Vanderbilt University\nSong Li\, University of California\, Los Angeles\nYekaterina Miorshnikova\, National Institutes of Health\nMaeli Melotto\, University of California\, Davis\nGuilherme Nader\, Children’s Hospital of Philadelphia\nVerena Ruprecht\, Centre for Genomic Regulation\n\n  \nFor inquiries\, please contact: \nSymposium logistics: Annie Jeong\, CEMB Managing Director (annjeong@seas.upenn.edu) \nScientific content: Vivek Shenoy\, CEMB Director (vshenoy@seas.upenn.edu) \nCEMB event website: https://cemb.upenn.edu/research/mechanobiology-annual-symposium/
URL:https://seasevents.nmsdev7.com/event/5th-annual-mechanobiology-symposium/
LOCATION:Smilow Center Auditorium\, 3400 Civic Center Blvd\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Conference
ORGANIZER;CN="Center for Engineering MechanoBiology (CEMB)":MAILTO:annjeong@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230404T100000
DTEND;TZID=America/New_York:20230404T113000
DTSTAMP:20260404T153916
CREATED:20230322T170727Z
LAST-MODIFIED:20230322T170727Z
UID:10007522-1680602400-1680607800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Material-Enabled Technologies for Soft and Fluidic Robots"
DESCRIPTION:The emerging field of soft robotics\, which incorporates unconventional or compliant materials in autonomous systems\, has simultaneously reshaped traditional robotics applications and introduced new use cases for robots. However\, many useful classes of materials remain relatively unexplored\, and furthermore\, the vast majority of soft robotics research has targeted actuation and sensing\, with power and control schemes still relying on bulky\, rigid electronic components. My research program addresses open questions in these domains by applying our expertise in energy\, fluids\, and materials. For instance\, biotic materials—non-living materials derived from living organisms—have remained underutilized in robotics\, despite having played a role in human development since the times our early ancestors wore animal hides as clothing and used bones for tools. In the first part of my talk\, I describe how we repurposed an inanimate spider as a ready-to-use actuator requiring only a single fabrication step\, initiating the area of “necrobotics” in which biotic materials are used as robotic components. The second part of my talk focuses on assistive wearable robots\, which currently rely on bulky and hard control systems and power supplies\, or alternatively require cumbersome tethers to external infrastructure. To address this limitation\, my group has developed completely soft fluidic digital logic components fabricated entirely from textiles. Our fluidic logic platform enables integrated memory\, decision making\, and the ability to interact with and adapt to stimuli and the environment\, all without the use of rigid valves or electronics. Meanwhile\, we address limitations in power delivery by developing “self-powered” textile-based wearable robots that harvest energy from the motion of the human body. The integration of fluidic logic and energy harvesting in textile architectures represents an important step toward fully soft\, self-sufficient wearable robots that are as comfortable\, resilient\, and practical as everyday clothing.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-material-enabled-technologies-for-soft-and-fluidic-robots/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 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:20230404T160000
DTEND;TZID=America/New_York:20230404T170000
DTSTAMP:20260404T153916
CREATED:20230313T182949Z
LAST-MODIFIED:20230313T182949Z
UID:10007512-1680624000-1680627600@seasevents.nmsdev7.com
SUMMARY:(Re)Connecting in the Classroom: Evan C Thompson Lecture on Excellence in Teaching
DESCRIPTION:Lately there has been much talk in universities and the public about “the great disengagement.” As we emerge from the pandemic era\, many students and instructors alike are experiencing a lack of energy and motivation\, and we are seeking encounters in the classroom that are transformative\, rather than simply transactional. \nIn this talk\, Daeyeon Lee will reflect on his own evolution as a teacher\, from his early focus on content delivery to his current practice of infusing classes with intrigue\, storytelling and humor. Speaking from his own experiences\, Lee will argue that creative exchanges with our students are just as important as content coverage in today’s classrooms. He will share some of his strategies for engaging students\, practices that have enabled him to establish meaningful relationships with his students while also maintaining high academic standards. He shows us that taking time to connect with each other socially and emotionally is an essential part of revitalizing our intellectual culture and community.
URL:https://seasevents.nmsdev7.com/event/reconnecting-in-the-classroom-evan-c-thompson-lecture-on-excellence-in-teaching/
LOCATION:Van Pelt Library\, Kislak Center\, 6th Floor\, 3420 Walnut Street\, Philadelphia\, 19104\, United States
CATEGORIES:Seminar,Faculty,Doctoral,Graduate,Student,Master's,Postdoctoral,Undergraduate
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230405T090000
DTEND;TZID=America/New_York:20230405T100000
DTSTAMP:20260404T153916
CREATED:20230327T194836Z
LAST-MODIFIED:20230327T194836Z
UID:10007533-1680685200-1680688800@seasevents.nmsdev7.com
SUMMARY:MEAM Ph.D. Thesis Defense: "Towards Digital Twins for Cardiovascular Flows: A Hybrid Machine Learning and Computational Fluid Dynamics Approach"
DESCRIPTION:To design personalized treatment strategies\, a number of measurable quantities (biomarkers) that relate a patient’s clinical representation to the existence\, progress\, and outcome of a disease need to be identified and measured. In cases where a biomarker is strongly correlated with the disease outcome\, e.g. vascular pressure for hypertension\, changes to the biomarker will perfectly describe changes of the disease outcome. However these ofter require invasive procedures to be measured. When the desired biomarkers correspond to physical properties\, computational mechanics can be leveraged to obtain predictions in-silico. Unfortunately\, computational models require a list of patient specific parameters\, such as precise boundary conditions\, which also cannot be easily measured in-vivo. Inaccurate calibration of these parameters is often the cause of poor predictions\, therefore hindering the translational impact of computational methods. These challenges motivate the need flexible and computationally efficient frameworks that can operate under uncertain model assumptions and partial measurements. \nThe goal of this thesis is to introduce a novel approach to precision medicine by synthesizing artificial intelligence (AI) and computational modeling. We start by exploring how one can use available patient data to estimate parameters in computational fluid dynamics models of arterial blood flow\, and show that this is prohibitively expensive. Then we accelerate the prediction of biomarkers by training surrogates to reconstruct available measurements by building physics-informed machine learning models to infer correlations between measurable (e.g.\, blood velocity) and unmeasurable quantities (e.g.\, vascular pressure) through underlying laws of fluid mechanics. We show that even though this is a successful approach it also faces challenges in generalizing to new clinical scenarios. Finally we propose a purely data-driven approach for making online biomarker predictions. In many biological scenarios the data acquisition process can be expensive and time consuming\, limiting the amount of available training data. For this purpose\, we propose creating a virtual patient database via computational fluid dynamics to train a neural operator model which we then use to make online predictions for new patients and clinical conditions. This computational efficiency that this brings has the potential to bridge the gap between modeling and clinical decision making.
URL:https://seasevents.nmsdev7.com/event/meam-ph-d-thesis-defense-towards-digital-twins-for-cardiovascular-flows-a-hybrid-machine-learning-and-computational-fluid-dynamics-approach/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Doctoral,Dissertation or Thesis Defense
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230405T120000
DTEND;TZID=America/New_York:20230405T133000
DTSTAMP:20260404T153916
CREATED:20230222T154650Z
LAST-MODIFIED:20230222T154650Z
UID:10007491-1680696000-1680701400@seasevents.nmsdev7.com
SUMMARY:ASSET Seminar: Lockout: Sparse Regularization of Neural Networks\, Gilmer Valdes (UCSF)
DESCRIPTION:ABSTRACT: \nMany regression and classification procedures fit a function f(x;w) of predictor variables x to data 〖{x_i\,y_i}〗_1^N based on some loss criterion L(y\,f(x;w)). Often\, regularization is applied to improve accuracy by placing a constraint P(w)≤t on the values of the parameters w\, where P is a monotonic increasing function of the absolute values of the parameters (e.g. Lasso\, Ridge). Although efficient methods exist for finding solutions to these constrained optimization problems for all values of t≥0 in the special case when f is a linear function\, none are available when f is non-linear (e.g. Neural Networks). Here we present a fast algorithm that provides all such solutions (path) for any differentiable function f and loss L\, and any differentiable constraint P that is an increasing monotone function of the absolute value of each parameter. Applications involving sparsity inducing regularization of arbitrary Neural Networks are discussed. Empirical results indicate that these sparse solutions are usually superior to their dense counterparts in both accuracy and interpretability (sometimes strikingly better). This improvement in accuracy can often make Neural Networks competitive with\, and sometimes superior to\, state-of-the-art methods in the analysis of tabular data. Specific applications to Medicine\, in particular\, Microarray data are discussed.
URL:https://seasevents.nmsdev7.com/event/asset-seminar-tba-gilmer-valdes-ucsf/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230405T150000
DTEND;TZID=America/New_York:20230405T160000
DTSTAMP:20260404T153916
CREATED:20230329T163056Z
LAST-MODIFIED:20230329T163056Z
UID:10007536-1680706800-1680710400@seasevents.nmsdev7.com
SUMMARY:Spring 2023 GRASP SFI: Matt Zucker\, Swarthmore College\, "Robotics education and research at a liberal arts college"
DESCRIPTION:This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom. This week’s presenter will be in-person as well. \nABSTRACT\nIn this talk\, I will describe the transition from a research-intensive PhD to a position at a teaching-focused undergraduate institution in the context of my own career trajectory. Key topics include connections between robotics and the liberal arts\, and guidelines for graduate students who are curious about pursuing teaching-focused faculty jobs.
URL:https://seasevents.nmsdev7.com/event/spring-2023-grasp-sfi-matt-zucker/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230406T103000
DTEND;TZID=America/New_York:20230406T113000
DTSTAMP:20260404T153916
CREATED:20230328T134651Z
LAST-MODIFIED:20230328T134651Z
UID:10007534-1680777000-1680780600@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Designing Quantum Properties and Functionalities in Heterostructures" (MIT)
DESCRIPTION:Interfaces in heterostructures often exhibit emergent physical properties that are absent in the constituent layers. These heterostructures\, together with their compatibility with nanofabrication\, are promising for constructing functional quantum devices. I will illustrate the capability of such heterostructure engineering by taking two examples. I will first explain our discovery of a versatile method for synthesizing ferroelectric materials from non-ferroelectric two-dimensional (2D) materials: by physically stacking two monolayer boron nitrides at controlled angles\, novel types of ferroelectricity emerge at the interface. I will demonstrate its functionality as one of the world’s thinnest ferroelectric non-volatile memories at room temperature. As another example\, I will demonstrate the versatile spintronic functionalities of topological insulator heterostructures synthesized by a thin film growth method. I will conclude by discussing how we can overcome the limits of existing heterostructure engineering for the future design of quantum materials and functionalities.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-designing-quantum-properties-and-functionalities-in-heterostructures-mit/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230406T130000
DTEND;TZID=America/New_York:20230406T150000
DTSTAMP:20260404T153916
CREATED:20230314T131648Z
LAST-MODIFIED:20230314T131648Z
UID:10007515-1680786000-1680793200@seasevents.nmsdev7.com
SUMMARY:BE Doctoral Dissertation Defense: "Investigating the Role of Lipid Droplets as Intracellular Mechanical Stressors in Non-alcoholic Fatty Liver Disease" (Abigail Lonker)
DESCRIPTION:The Department of Bioengineering at the University of Pennsylvania and Dr. Rebecca Wells are pleased to announce the Doctoral Dissertation Defense of Abigail Lonker. \nTitle: Investigating the Role of Lipid Droplets as Intracellular Mechanical Stressors in Non-alcoholic Fatty Liver Disease \nDate: April 6\, 2023\nTIme: 1:00 PM\nLocation: Smilow Center for Translational Research in SCTR 11-146AB \nThe public is welcome to attend.
URL:https://seasevents.nmsdev7.com/event/be-doctoral-dissertation-defense-investigating-the-role-of-lipid-droplets-as-intracellular-mechanical-stressors-in-non-alcoholic-fatty-liver-disease-abigail-lonker/
LOCATION:Smilow Center for Translational Research in SCTR 11-146AB
CATEGORIES:Doctoral,Graduate,Student,Dissertation or Thesis Defense
ORGANIZER;CN="Bioengineering":MAILTO:be@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230406T143000
DTEND;TZID=America/New_York:20230406T153000
DTSTAMP:20260404T153916
CREATED:20230328T194407Z
LAST-MODIFIED:20230328T194407Z
UID:10007535-1680791400-1680795000@seasevents.nmsdev7.com
SUMMARY:IoT4Ag Seminar: Sensing and Robotics for Specialty Crop Production
DESCRIPTION:Register to attend via Zoom
URL:https://seasevents.nmsdev7.com/event/iot4ag-seminar-sensing-and-robotics-for-specialty-crop-production/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230407T140000
DTEND;TZID=America/New_York:20230407T150000
DTSTAMP:20260404T153916
CREATED:20230327T140734Z
LAST-MODIFIED:20230327T140734Z
UID:10007531-1680876000-1680879600@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Deep Anomaly Detection using Coincident Learning"
DESCRIPTION:Anomaly detection is a crucial task in the operation of complex systems such as industrial facilities\, manufacturing plants\, and large-scale science experiments. Failures in a sub-system can result in low yield\, faulty products\, or damage to components\, making it essential to detect anomalies as quickly as possible. Despite the abundance of data available for complex systems\, labeled anomalies are rare and expensive to obtain. To address this issue\, we present a novel approach called CoAD that trains anomaly detection models on unlabeled data by leveraging the correlation between sub-systems and products. CoAD works by analyzing two data streams\, s and q\, which represent subsystem diagnostics and final product quality\, respectively. We define an unsupervised metric\, akin to the supervised classification F_beta statistic\, to assess the performance of independent anomaly detection algorithms on s and q based on their coincidence rate. Our method is demonstrated in four cases\, including a synthetic outlier data set\, a synthetic imaging data set generated from MNIST\, a metal milling data set\, and a data set obtained from a particle accelerator. By using CoAD\, we can detect anomalies in complex systems more effectively\, even when labeled anomalies are scarce. \nEmail jnespos@seas.upenn.edu for the Zoom link.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-deep-anomaly-detection-using-coincident-learning/
LOCATION:https://upenn.zoom.us/j/96715197752
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
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