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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231109T110000
DTEND;TZID=America/Detroit:20231109T130000
DTSTAMP:20231115T151403Z
CREATED:20231102T175811Z
LAST-MODIFIED:20231115T151403Z
UID:10000661-1699527600-1699534800@micde.umich.edu
SUMMARY:SciML Webinar: Lenz Fiedler - Efficient calculations of electronic structures with machine-learning models
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Lenz Fiedler (Helmholtz-Zentrum Dresden-Rossendorf)\nSession Chair: Michael Herbst (EPFL) \nAbstract: Quantum mechanical calculations of the electronic structure of matter enable accessing interesting thermodynamical properties without the need for prior experimental measurements. Therefore\, electronic structure calculations are of great interest in fields such as materials discovery or drug design. At the forefront of such simulations lies density functional theory (DFT)\, due to its excellent balance between computational accuracy and efficiency. Yet\, as pressing environmental and social issues shift the research focus to increasingly complicated systems and conditions\, even the most efficient of DFT implementations are approaching their limitations in terms of computational feasibility. A possible route to enable more complex calculations lies with machine learning (ML)\, i.e.\, algorithms that are capable of capturing complicated relationships based on large amounts of data.\nIn this talk\, Lenz Fiedler will talk about current contributions of Center for Advanced Systems Understanding\, Helmholtz-Zentrum Dresden-Rossendorf (CASUS) w.r.t. building ML models that replace conventional DFT calculations. More precisely\, Lenz will talk about the current state of the Materials Learning Algorithms library (MALA)\, which allows easy training and inference for ML-DFT models that are developed by CASUS in cooperation with Sandia National Laboraties and Oak Ridge National Laboratory. In contrast to comparable frameworks\, MALA allows full access to the electronic structure of compounds\, including volumetric data as well as scalar quantities of interest\, such as energies. It will be shown how MALA models can operate efficiently across phase boundaries\, length scales and temperature ranges.
URL:https://micde.umich.edu/event/sciml-webinar-lenz-fiedler-efficient-calculations-of-electronic-structures-with-machine-learning-models/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231026T110000
DTEND;TZID=America/Detroit:20231026T130000
DTSTAMP:20231107T231334Z
CREATED:20231017T170318Z
LAST-MODIFIED:20231107T231334Z
UID:10000658-1698318000-1698325200@micde.umich.edu
SUMMARY:SciML Webinar: Bowen Deng - CHGNet: pretrained universal interatomic potential to study electron coupled ionic systems.
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Bowen Deng (UC Berkeley)\nSession Chair: Sakidja Ridwan (Missouri State University) \nAbstract: Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modeling. Although classical force fields often fail to describe the\ncoupling between electronic states and ionic rearrangements\, the more accurate ab-initio molecular dynamics suffers from computational complexity that prevents long-time and large-\nscale simulations\, which are essential to study technologically relevant phenomena. Our work presents the Crystal Hamiltonian Graph Neural Network (CHGNet)\, a graph-neural-\nnetwork-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies\, forces\, stresses\, and magnetic moments\nfrom the Materials Project Trajectory Dataset\, which consists of over 10 years of density functional theory calculations of ∼ 1.5 million inorganic structures. The explicit inclusion of\nmagnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons\, enhancing its capability to describe both atomic and electronic degrees of freedom.\nWe demonstrate several applications of CHGNet in solid-state materials and energy storage applications.
URL:https://micde.umich.edu/event/sciml-webinar-bowen-deng/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231019T110000
DTEND;TZID=America/Detroit:20231019T130000
DTSTAMP:20231025T194805Z
CREATED:20230915T150343Z
LAST-MODIFIED:20231025T194805Z
UID:10000646-1697713200-1697720400@micde.umich.edu
SUMMARY:SciML Webinar Ji Qi: DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust Training of Machine Learning Interatomic Potentials
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Ji Qi (UC San Diego and LLNL)\nSession Chair: Daniel Schwalbe-Koda (UC Los Angeles) \nAbstract: Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond conventional first-principles approaches\, and they have played increasingly important roles in understanding and design of materials. However\, MLIPs are only as accurate and robust as the data they are trained on. In this seminar\, I will present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements\, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive ab initio MD to rapidly create a large configuration space for target materials systems. For demonstration\, we combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. \nIn this seminar\, I will walk through two Jupiter notebooks to showcase DIRECT sampling with the two example cases demonstrated in our manuscript\, so that audience can expect to reproduce our major results with no trouble. Hopefully\, DIRECT sampling will serve as a straightforward\, efficient\, useful plug-in for the robust training of MLIPs across any compositional complexity.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-ji-qi-2/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231012T110000
DTEND;TZID=America/Detroit:20231012T130000
DTSTAMP:20231018T173554Z
CREATED:20230915T150330Z
LAST-MODIFIED:20231018T173554Z
UID:10000644-1697108400-1697115600@micde.umich.edu
SUMMARY:SciML Webinar Justin Beroz: A closed-form mathematical framework for modeling turbulent fluids
DESCRIPTION:Speaker: Justin Beroz (ReynKo Inc.) \n\nSession Chair: Varun Shankar (Physics Inverted Mataerials) \nAbstract: Despite significant advances over the past two centuries\, a complete general mathematical framework for turbulent fluid motion has yet to be put forth\, and remains the longest standing unsolved problem in classical physics. I will present such a framework\, which is based on constructing a spectral decomposition for the fluid’s kinetic energy from first principles. The approach departs from the usual Reynolds decomposition and yields a set of closed and solvable ordinary differential equations in matrix form. Within this prescription\, the linear terms in the Navier-Stokes equations correspond to a symmetric matrix operator\, and the nonlinear convective term enters as an anti-symmetric operator that provides coupling between eigenstates of turbulent fluctuation. Specifically\, I will present a derivation for the turbulent energy spectrum\, including the Kolmogorov energy cascade; elucidate instability mechanisms for the transition to turbulence;  and detail the analytical solution for turbulence in a box. Careful attention will be given to the physical picture and scaling\, in addition to the rigorous mathematical program. The talk will conclude with a forward look into current efforts implementing the model into a numerical simulation within my company\, ReynKo Inc.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-justin-beroz/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210604T100000
DTEND;TZID=America/Detroit:20210604T110000
DTSTAMP:20230905T171444Z
CREATED:20230905T171444Z
LAST-MODIFIED:20230905T171444Z
UID:10000483-1622800800-1622804400@micde.umich.edu
SUMMARY:Marc Henry de Frahan Webinar
DESCRIPTION:Title: Leveraging modeling hierarchies in the Exascale era: applications to combustion technologies \nAbstract: As we approach the confluence of widespread use of machine learning techniques and simulations running at exascale\, several important challenges will need to be addressed. In this talk\, we explore some of these challenges\, with a specific focus on combustion applications. We discuss a combustion simulation code\, PeleC\, and its performance characteristics on the fastest supercomputers available today. We look at leveraging the resulting high-fidelity simulations to construct data-driven models for lower-fidelity simulations. We then examine how to adapt reinforcement learning methods to explore a modeling hierarchy and determine adequate control strategies for combustion technologies. \nBio: Marc Henry de Frahan is a computational scientist at the National Renewable Energy Laboratory\, where he works on improving next-generation wind and combustion processes. As part of the Exascale Computing Project\, Marc develops high-fidelity turbulence models to enhance simulation accuracy and efficient numerical algorithms for future high-performance computing hardware architectures. In addition to traditional physics-based modeling\, he is integrating deep neural networks into modeling and reinforcement learning into advanced control strategies. Marc obtained his PhD in Mechanical Engineering in 2016 from the University of Michigan. \n\nZoom information to connect: \nLink: https://umich.zoom.us/j/98133041706 \nPasscode: 762808
URL:https://micde.umich.edu/event/marc-henry-de-frahan-webinar/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,Seminar,Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/marc-henry-de-frahan.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210506T090000
DTEND;TZID=America/Detroit:20210506T163000
DTSTAMP:20260612T023811Z
CREATED:20230905T171444Z
LAST-MODIFIED:20260612T023811Z
UID:10000469-1620291600-1620318600@micde.umich.edu
SUMMARY:Computing our way out of a pandemic: modeling in the face of COVID-19
DESCRIPTION:The COVID-19 pandemic has produced massive amounts of information that require an accurate analysis to predict outcomes and design solutions rapidly. It also has required experts from many different backgrounds to rally around in the quest for rapid responses in the race to save lives. \nMany of the most prominent of these researchers are from Michigan\, and a significant number of them are computational scientists who addressed questions such as: What measures should be taken to minimize contagion? Is it safe to ride a bus? How are supply and demand chains being affected? \nThis virtual symposium will bring together researchers from the State of Michigan to share their past and future insights into the pandemic. \n\nView additional event details. \nView event agenda. \nThis is a free Zoom event\, open to the general public. Please register to attend.
URL:https://micde.umich.edu/event/covid-19-modeling-seminar/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Conference,Featured Events,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201002T090000
DTEND;TZID=America/Detroit:20201002T100000
DTSTAMP:20230905T171254Z
CREATED:20230905T171254Z
LAST-MODIFIED:20230905T171254Z
UID:10000422-1601629200-1601632800@micde.umich.edu
SUMMARY:Neuroimaging Initiative webinar: Bayesian scalar-on-image neural networks with application to neuroimaging data
DESCRIPTION:About Dr. Kang: Jian Kang is a Professor in the Department of Biostatistics and is a faculty member of the Kidney Epidemiology and Cost Center (KECC) at the University of Michigan.  He received his PhD in Biostatistics from the University of Michigan in 2011. He was an Assistant Professor in the Department of Biostatistics and Bioinformatics and the Department of Radiology and Imaging Sciences at Emory University from 2011 – 2015. He was a core faculty member in the Center for Biomedical Imaging Statistics (CBIS) at Emory University.  His primary research interests are in developing statistical methods for large-scale complex biomedical data with application in precision medicine\, imaging\, epidemiology and genetics. \n\nBAYESIAN SCALAR-ON-IMAGE NEURAL NETWORKS WITH APPLICATION TO NEUROIMAGING DATA\nDeep neural networks have been adopted in the scalar-on-image regression which predicts the outcome variable using image predictors. However\, training DNN often requires a large sample size to achieve a good prediction accuracy and the model fitting results can be difficult to interpret. In this work\, we construct a novel single-layer Bayesian neural network (BNN) with spatially-varying coefficients (SVC) for the scalar-on-image regression. Our goal is to select interpretable image features and to achieve the high prediction accuracy with limited training samples. We assign the soft-thresholded Gaussian process (STGP) prior to the SVCs and develop an efficient posterior computation algorithm based on stochastic gradient Langevin Dynamics (SGLD). The BNN-STGP provides a large prior support for sparse\, piecewise-smooth and continuous SVCs\, enabling efficient posterior inference on image feature selection and automatically determining the network structures. We establish the posterior consistency of estimating the SVCs in the model and image feature selection consistency when the number of voxels/pixels grows much faster than the sample size. We compared our methods with state-of-the-art deep learning methods via extensive simulations and analyses of multiple real datasets including the task fMRI data from the ABCD study. \n\nConnect to the webinar via this link. Meeting ID: 923 3875 2870. Meeting Passcode: 149254
URL:https://micde.umich.edu/event/bayesian-scalar-on-image-neural-networks-with-application-to-neuroimaging-data/
LOCATION:Zoom Event
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Jian-Kang-Headshot.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200602T150000
DTEND;TZID=America/Detroit:20200602T160000
DTSTAMP:20230905T171345Z
CREATED:20230905T171345Z
LAST-MODIFIED:20230905T171345Z
UID:10000375-1591110000-1591113600@micde.umich.edu
SUMMARY:MICDE Webinar Series: Gabriel Ehrlich\, Director\, Research Seminar in Quantitative Economics\, University of Michigan
DESCRIPTION:Bio: Dr. Gabriel Ehrlich is the Director of the Research Seminar in Quantitative Economics (RSQE)\, and an Assistant Research Scientist in the department of Economics at the University of Michigan. Prior to joining RSQE\, he worked in the Financial Analysis Division at the Congressional Budget Office (CBO)\, where he forecast interest rates and conducted analysis on monetary policy and the mortgage finance system. His academic research focuses on several areas of housing and land economics as well as the effects of wage rigidity on labor market outcomes. \nMODELING THE ECONOMIC OUTLOOK IN THE TIME OF COVID-19\nWe will present the Research Seminar in Quantitative Economics’ (RSQE’s) forecast for the national and Michigan economies from 2020 to 2022. We will discuss the incoming data during the COVID-19 pandemic\, the near-term economic damage\, and the prospects for economic recovery. RSQE is the world’s oldest continuously operating economic forecasting unit and is home to the “Michigan Model” of the U.S. economy. \nWatch the full seminar.
URL:https://micde.umich.edu/event/webinar-gabriel-ehrlich-2020/
CATEGORIES:Featured Events,Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Ehrlich.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200512T130000
DTEND;TZID=America/Detroit:20200512T140000
DTSTAMP:20260522T153619Z
CREATED:20230905T171344Z
LAST-MODIFIED:20260522T153619Z
UID:10000364-1589288400-1589292000@micde.umich.edu
SUMMARY:CAsToR Webinar: Modeling in Tobacco Control in the U.S. - the good\, the bad\, the ugly
DESCRIPTION:Center for the Assessment of Tobacco Regulations [CAsToR] leads Drs. Levy\, Mendez\, and Meza will provide an overview of modeling applications in tobacco control research\, discuss the types of models used in this field and their purpose\, as well as future directions for modeling in tobacco regulatory science. A Q&A session will follow. \nPlease contact Katie Zarins (kmrents@umich.edu) with questions \nDr. David Levy\nProfessor\nGeorgetown University\n  \n  \n  \n \nDr. David Mendez\nAssociate Professor\nUM School of Public Health\n  \n  \n  \n \nRafael Meza\nAssociate Professor\nUM School Public Health\n  \n  \n  \n 
URL:https://micde.umich.edu/event/castor-webinar-modeling-in-tobacco-control-in-the-u-s-the-good-the-bad-the-ugly/
LOCATION:Zoom Event
CATEGORIES:Featured Events,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200417T150000
DTEND;TZID=America/Detroit:20200417T163000
DTSTAMP:20230905T171344Z
CREATED:20230905T171344Z
LAST-MODIFIED:20230905T171344Z
UID:10000606-1587135600-1587141000@micde.umich.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200409T100000
DTEND;TZID=America/Detroit:20200409T113000
DTSTAMP:20230905T171344Z
CREATED:20230905T171344Z
LAST-MODIFIED:20230905T171344Z
UID:10000356-1586426400-1586431800@micde.umich.edu
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/
CATEGORIES:Featured Events,Webinar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/jzelner-e1584116599101.jpg
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