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DTSTART;TZID=America/Detroit:20171201T083000
DTEND;TZID=America/Detroit:20171201T173000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
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SUMMARY:2017 U-M Data Science Research Forum
DESCRIPTION:Forum Highlights \n\nOral and poster presentations on\n\nTheoretical foundations of data science\nData science methodology\nData science applications in any research domain\nSocial impact of data science research\n\n\n\n\nHow to engage industry workshop\n\nAdrian Fortino\, Partner (Mercury Fund)\nMike Psarouthakis\, Director (U-M Venture Center)\nKevyn Collins-Thompson\, Associate Professor\, U-M School of Information\nMike Cafarella\, Associate Professor\, U-M Computer Science Engineering\n\n\n\n\nKeynote by Chris Rozell\n\n\nNetworking Reception\n\nAll presentations will come from submissions in response to our call for abstracts\n Abstract Submission Deadline: October 23\, 2017\n We welcome submission from all U-M data science researchers (faculty\, staff\, trainees) \nPlease register for this event.  Please also see the call for abstracts for instruction\, and submit through the Abstract Submission Form. \nPreliminary Schedule
URL:https://micde.umich.edu/event/data-science-research-forum/
LOCATION:Michigan League\, 911 N. University\, Ann Arbor\, MI\, 48104\, United States
CATEGORIES:Conference,Data Science,Seminar
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DTSTART;TZID=America/Detroit:20171201T180000
DTEND;TZID=America/Detroit:20171201T190000
DTSTAMP:20230905T171416Z
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SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop/2017-12-01/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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GEO:42.292322;-83.713272
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171204T100000
DTEND;TZID=America/Detroit:20171204T110000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series: Maziar Raissi\, Brown University
DESCRIPTION:Bio: Maziar Raissi is an Assistant Professor of Applied Mathematics (research) in the Division of Applied Mathematics at Brown University. He received his Ph.D. in Applied Mathematics & Statistics\, and Scientific Computations from University of Maryland — College Park in December 2016. Raissi’s expertise lies at the intersection of Probabilistic Machine Leaning\, Deep Learning\, and Data Drive Scientific Computing. \nHidden Physics Models: Machine Learning of Non-linear Partial Differential Equations\n\nA grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws\, physical principles\, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering\, science\, and technology. At the intersection of probabilistic machine learning\, deep learning\, and scientific computations\, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision\, this work is exploring two complimentary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics\, expressed by time dependent and non-linear differential equations\, to extract patterns from high-dimensional data generated from experiments\, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data\, infer latent quantities of interest (e.g.\, the solution to a differential equation)\, and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics. \n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-maziar-raissi-brown-university/
CATEGORIES:Seminar
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DTSTART;TZID=America/Detroit:20171205T150000
DTEND;TZID=America/Detroit:20171205T160000
DTSTAMP:20230905T171416Z
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SUMMARY:MICDE Seminar: Tarek Zohdi\, Department of Mechanical Engineering\, University of California\, Berkeley
DESCRIPTION:Bio: Tarek I. Zohdi received his Ph.D. in 1997 in Computational and Applied Mathematics from the University of Texas at Austin and his Habilitation in General Mechanics from the Gottfried Leibniz University of Hannover in 2002. He is currently a Chancellor’s Professor of Mechanical Engineering\, Chair of the Computational and Data Science and Engineering Program at UC Berkeley and holder of the W. C. Hall Family Endowed Chair in Engineering. He also holds a Staff Scientist position at Lawrence Berkeley National Labs. His main research interests are in computational approaches for advanced manufacturing and nonconvex multiscale-multiphysics inverse problems\, in particular addressing the issue of how large numbers of micro-constituents interact to produce macroscale aggregate material behavior. He has published over 145 archival refereed journal papers and five books. In 2000\, he received the Zienkiewicz Prize and Medal\, which are awarded once every two years\, to one post-graduate researcher under the age of 35\, by The Institution of Civil Engineers in London\, to commemorate the work of Professor O. C. Zienkiewicz\, for research which contributes most to the field of numerical methods in engineering. In 2002\, he received the Best Paper of the Year 2001 Award in London\, at the Lord’s Cricket Grounds\, for a paper published in Engineering Computations\, pertaining to modeling and simulation of the propagation of failure in particulate aggregates of material. In 2003\, he received the Junior Achievement Award of the American Academy of Mechanics. The award is given once a year\, to one post-graduate researcher\, to recognize outstanding research during the first decade of a professional career. In 2008\, he was elected Fellow of the International Association for Computational Mechanics (IACM) and in 2009 he was elected Fellow of the United Stated Association for Computational Mechanics (USACM). He was elected President of the USACM in 2012\, and served from 2012 to 2014. He is an editor of Computational Mechanics\, Editor in Chief of Computational Particle Mechanics and serves on 12 editorial boards of international journals. For more information visit http://www.me.berkeley.edu/people/faculty/tarek-i-zohdi \nModeling and Simulation of Multistage Multiphysical Processes in Next-Generation Advanced Manufacturing and 3D Printing with New Multifunctional Materials\nWithin the last decade\, several industrialized countries have stressed the importance of advanced manufacturing to their economies. Many of these plans have highlighted the development of additive manufacturing techniques\, such as 3D printing\, which are still in their infancy. The objective is to develop superior products\, produced at lower overall operational costs. For these   goals to be realized\, a deep understanding of the essential ingredients comprising the materials involved in additive manufacturing is needed. The combination of rigorous material modeling theories\, coupled with the dramatic increase of computational power can potentially play a significant role in the analysis\, control\, and design of many emerging additive manufacturing processes. Specialized materials and the precise   design of their properties are key factors in the processes. Specifically\, particle-functionalized materials play a central role in this field\, in three main ways:   (1) to endow filament-based materials by adding particles to a heated binder   (2) to “functionalize” inks by adding particles to freely flowing solvents and (3) to directly deposit particles\, as dry powders\, onto surfaces and then to heat them with a laser\, e-beam or other external source\, in order to fuse them into place. The goal of these processes is primarily to build surface structures\, coatings\, etc.\, which are extremely difficult to construct using classical manufacturing methods. The objective of this presentation is to introduce the audience to basic techniques which can allow them to rapidly develop and analyze particulate-based materials needed in new additive manufacturing processes. This presentation is broken into two main parts: continuum and discrete element approaches. The materials associated with methods (1) and (2) are closely related types of continua (particles embedded in a continuous binder) and are treated using continuum approaches. The materials in method (3)\, which are of a discrete particulate character\, are analyzed using discrete element methods. \nProf. Zohdi is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him please email mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-tarek-zohdi-department-of-mechanical-engineering-university-of-california-berkeley/
LOCATION:1109 FXB\, 1320 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20171206T120000
DTEND;TZID=America/Detroit:20171206T130000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series: Xun (Ryan) Huan\, Combustion Research Facility at Sandia National Laboratories
DESCRIPTION:Bio: Xun (Ryan) Huan is a postdoctoral researcher in the Combustion Research Facility at Sandia National Laboratories. He received a Ph.D. in Computational Science and Engineering from MIT Department of Aeronautics and Astronautics. He also has a master’s degree from MIT and a bachelor’s degree from the University of Toronto\, both in Aerospace Engineering. Xun’s research interests broadly revolve around uncertainty quantification\, decision-making under uncertainty\, data-driven modeling\, and optimization for engineering applications. Outside work\, he is an ice hockey player and a pilot. \nFinding the Most Informative Data Using Model-based Optimal Experimental Design\nExperimental data play a crucial role in developing and refining models of physical systems. However\, some experiments produce more useful data than others\, and well-chosen experiments can provide substantial resource savings. Optimal experimental design (OED) thus seeks to systematically quantify and maximize the value of experiments. We introduce general mathematical frameworks and algorithmic approaches for OED with nonlinear models. The formalism employs Bayesian statistics and an information-theoretic objective\, and rigorously defines the conditions under which batch experiments (experiments chosen simultaneously) and sequential experiments (forward-looking designs with data feedback) are truly optimal. Finding these optimal designs using conventional means is generally intractable. We develop practical numerical methods for OED by advancing computational techniques on several fronts\, including stochastic optimization\, polynomial chaos surrogate modeling\, approximate dynamic programming\, and transport maps. Using the overall algorithm\, we design combustion experiments for optimal learning of Arrhenius kinetic parameters\, and sequential sensor placement for contaminant source inversion.\n\n* Lunch won’t be provided but you are welcome to bring your own\n\n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xun-ryan-huan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/XunRyanHuan-e1583777526832.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171211T100000
DTEND;TZID=America/Detroit:20171211T110000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series:  Xiu Yang\, Computational Mathematics Scientist\, Pacific Northwest National Laboratory
DESCRIPTION:Bio: Dr. Xiu Yang received his B.S. and M.Sc. in computational mathematics from Peking University\, Beijing\, China and Ph.D. in applied mathematics from Brown University\, Providence\, RI. He is currently a research scientist in computational mathematics group at Pacific Northwest National Laboratory\, Richland\, WA. His research interests include uncertainty quantification\, multi-scale modeling\, multi-fidelity data fusion and inverse problem.\n  \n Uncertainty Quantification for Complex Systems Using Limited Data\nRealistic analysis and design of complex engineering systems require not only a fine understanding of the underlying physics\, but also a significant recognition of uncertainties and their influences on the quantities of interest. Intrinsic variabilities and lack of knowledge about system parameters or governing physical models often considerably affect quantities of interest and decision-making processes. For complex systems\, the available data for quantifying uncertainties or analyzing sensitivities are usually limited because the cost of conducting a large number of experiments or running many large-scale simulations can be prohibitive. Efficient approaches of representing uncertainties using limited data are critical for such problems. I will talk about three methods for uncertainty quantification by constructing surrogate model of the quantity of interest. The first method is the adaptive functional ANOVA method\, which constructs the surrogate model hierarchically by analyzing the sensitivities of individual parameters. The second method is the sparse regression based on identification of low-dimensional structure\, which exploits low-dimensional structures in the parameter space and solves an optimization problem to construct the surrogate model. The third one is the multi-fidelity information fusion via Gaussian process regression\, which integrates limited high-fidelity data with a large number of low-fidelity data. I will demonstrate the efficiency of these methods in applications including perturbation of drag and lift in aerodynamics\, solvation energy computing in chemical biology\, stability analysis of power grid system and optimizing Li-O2 battery design. \n  \n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xiu-yang-computational-mathematics-scientist-pacific-northwest-national-laboratory/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
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