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SUMMARY:Webinar: 2020 MICDE Catalyst Grants Showcase - Session I
DESCRIPTION:This webinar will showcase some of the game-changing research supported by our Catalyst Grants program. \nThis event was recorded and will be on the UM Youtube channel shortly. \nSpeakers\n \nRobert Krasny\nProfessor of Applied Mathematics and Mathematics\nUniversity of Michigan\nINTEGRAL EQUATION BASED METHODS FOR SCIENTIFIC COMPUTING\nThere are several different approaches to the numerical solution of partial differential equations. For example\, finite-difference methods and finite-element methods discretize either the strong form or the weak form of the equation in real space\, while spectral methods discretize the equation in reciprocal space. This project employs an alternative method which converts the differential equation into an integral equation by convolution with the Green’s function\, followed by discretization and linear solution; the hope is that this approach is more amenable to adaptive refinement and parallelization than other methods. In the past\, integral equation based methods were hindered by the difficulty of discretizing singular integrals and the cost of computing dense matrix-vector products\, but these obstacles are being brought under control. We present our recent work in this area including (1) a GPU-accelerated barycentric treecode for long-range particle interactions\, (2) applications in electrostatics\, electronic structure\, and vortex dynamics. \n\nVikram Gavini\nProfessor of Mechanical Engineering\nUniversity of Michigan\nLong time-scale simulations using exponential time-propagators\nHigh-fidelity long-time scale simulations have been a challenge in a wide range of areas\, including time-dependent electronic structure calculations and molecular dynamics. In particular\, time-dependent density functional theory (TDDFT) calculations are limited to time-scales of the order of hundred femtoseconds\, and MD simulations (even those based on interatomic potentials) are routinely limited to time-scales of the order of nanoseconds. However\, there is very rich material phenomena\, both at the quantum and atomistic scale\, that occurs at time-scales that are orders of magnitude larger than the currently accessible range. In this talk\, I will present the ideas we have been exploring as part of the MICDE catalyst grant to enable long time-scale simulations on a class of time-dependent problems. In particular\, we investigate the use of exponential time-propagators as an alternative to the finite-difference based time-discretization of the PDEs. The ideas will be presented for time-dependent density functional theory and elastodynamics—as a prototypical problem for molecular dynamics—along with numerical results demonstrating the viability and computational efficiency of the proposed ideas. \nThis is joint work with Bikash Kanungo and Paavai Pari. \n\n \nYulin Pan\nAssistant Professor of Naval Architecture and Marine Engineering\nUniversity of Michigan\nReal-Time Phase-resolved ocean wave forecast with data assimilation enabled by gpu-accelerated computation\nThe real-time phase-resolved prediction of ocean waves is crucial for the safety of offshore operations. With the development of the remote sensing technology\, it is now possible to reconstruct the phase-resolved ocean surface from radar measurements in real time. Using the reconstructed ocean surface as initial condition\, nonlinear wave models such as the high-order spectral (HOS) method can be applied to predict the evolution of the ocean waves. However\, the computations reply heavily on large CPU clusters which are usually not available in the offshore onboard environment\, and the prediction can deviate quickly from the true wave evolution due to the chaotic nature of the nonlinear wave equations. To address these problems\, we develop a novel GPU-accelerated computational framework\, which features the coupling of HOS and an ensemble Kalman filter (EnKF) to reduce the uncertainties in the prediction. The new framework algorithm is tested and validated using both synthetic and real wave data\, and is shown promising in fundamentally improving the real-time prediction capability of ocean waves.
URL:https://micde.umich.edu/event/catalyst-grants-webinar-session-1/
LOCATION:MI
CATEGORIES:Featured Events,Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/jzelner-e1584116599101.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200409T130000
DTEND;TZID=America/Detroit:20200409T143000
DTSTAMP:20260627T035216
CREATED:20230905T171344Z
LAST-MODIFIED:20260522T183951Z
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SUMMARY:Webinar: 2020 MICDE Catalyst Grants Showcase - Session II
DESCRIPTION:This webinar will showcase some of the game-changing research supported by our Catalyst Grants program. \nThis event was recorded and will be on the UM Youtube channel shortly. \nSpeakers\n \nStephen Smith\nAssociate Professor of Ecology and Evolutionary Biology\nUniversity of Michigan\nThe Emergence of Biological Complexity and Evolutionary Innovation in Plant Genomes\n\nXun Huan\nAssistant Professor of Mechanical Engineering\nUniversity of Michigan\nTowards Bayesian Uncertainty Quantification in Deep Learning Models for Brain Tumor Segmentation\nWhile the use of deep learning (DL) models in healthcare has grown rapidly in recent years\, the uncertainty/confidence information in their predictions is often unavailable and unreported. A lack of such information can render decision-making dangerous\, and prompt clinicians to hesitate in using and trusting these machine learning technologies. We propose to adopt principles and computational methods of uncertainty quantification for medical artificial intelligence applications\, focusing on a problem of brain tumor segmentation from MRI scans. As a first step\, we assess the robustness and sensitivity of two such DL models\, U-Net and SqueezeU-Net\, with respect to uncertainty in model weights\, which may arise due to sparsity and noise in training data features as well as labels. We achieve this through Monte Carlo uncertainty propagation of noise injected on trained weight values. The resulting uncertainty of segmentation maps can then be presented and visualized through robustness maps and summarizing box-plots of the Dice coefficients\, which can help indicate the regions where our models do not predict well and most susceptible to training noise. In our on-going work\, we seek to compute the Bayesian posterior distributions for the weights directly from training data. However\, performing a full-scale inference for the millions of weights in U-Net and SqueezeU-Net would be prohibitive. Instead\, we develop a procedure to use sensitivity analysis to identify the most important subset of weights (or layers)\, and perform a targeted Bayesian inference on this lower-dimensional parameter space. \n\nMonica Valluri\nResearch Professor of Astronomy\nUniversity of Michigan\nProbing the nature of dark matter by modeling the Milky Way\nDespite nearly four decades of research in astrophysics and particle physics\, the nature of dark matter\, the substance that comprises 85% of the matter in the universe\, is unknown. The shape of the Milky Way’s dark matter distribution and the variation of this shape with radius are important probes of the nature of dark matter. Mapping the detailed formation history of the Milky Way\, especially the number of satellites that were assimilated by our Galaxy and their masses and their time of infall will provide clues to the dark matter distribution in satellites as well as evidence for nearby streams and dark matter satellites. We are developing a multi-pronged approach to understanding the nature of dark matter with new dynamical tools\, new simulations and analysis of large cosmological simuations. I will describe progress on our efforts to enhance the galactic dynamics package AGAMA (Vasiliev\, 2019)by adding GPU acceleration for the potential and action solvers. I will provide an update on how we are using positions and velocities for old stars in the Milky Way’s halo to determine the three dimensional shape of the dark matter distribution and its variation with radius.I will describe new simulations of the evolution of satellites that merge with our Milky Way that can lead to insights into the fundamental nature of dark matter. Finally I will descibe the use of two cluster finding tools (a self organizing mapping and multi-dimensional density estimation)\, that when applied to action-space properties of stars in the Milky Way’s halo\, can yield insights into the accretion history of our Galaxy. This concert of efforts will significantly advance our goal of understanding the fundamental nature of dark matter using the properties of stars in the Milky Way.
URL:https://micde.umich.edu/event/catalyst-grants-webinar-session-2/
LOCATION:MI
CATEGORIES:Featured Events
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200417T150000
DTEND;TZID=America/Detroit:20200417T163000
DTSTAMP:20260627T035216
CREATED:20230905T171344Z
LAST-MODIFIED:20230905T171344Z
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SUMMARY:Webinar: Transmission modeling of infectious diseases and the COVID-19 outbreak
DESCRIPTION:This seminar will focus on differential equation transmission modeling approaches to analyze the spread of infections diseases\, and how Prof. Eisenberg and her colleagues are using them to model the current COVID-19 outbreak in the State of Michigan.Their current model is helping to forecast the numbers of laboratory-confirmed cases\, fatalities\, hospitalized patients\, and hospital capacity issues (such as ICU beds needed)\, and examining how social distancing can impact the spread of the epidemic.
URL:https://micde.umich.edu/event/webinar-transmission-modeling-of-infectious-diseases-and-the-covid-19-outbreak/
LOCATION:BlueJeans Events
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Webinar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/07/Marisa-Eisenberg.png
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