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SUMMARY:Midwest Numerical Analysis Day 2022
DESCRIPTION:[vc_row type=”in_container” full_screen_row_position=”middle” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][vc_column_text]The Midwest Numerical Analysis Day (MWNADay) is a forum for researchers at all stages of their careers\, mainly from the Midwest\, to exchange ideas in numerical analysis\, scientific computing and related application areas. \nThis year it will take place in person and on line at the University of Michigan\, Ann Arbor on May 20 & May 21.\nParticipants are invited to give a contributed talk or present a poster. Participation of graduate students and postdocs is encouraged. Partial travel support is available. \nFor more information and to register\, please visit the event’s site. \n\nMWNAD 2022 is sponsored by the University of Michigan Department of Mathematics\, the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and the Michigan Institute for Computational Discovery and Engineering (MICDE). \nEmail questions about this year’s event to MWNADadmin@umich.edu.[/vc_column_text][/vc_column][/vc_row]
URL:https://micde.umich.edu/event/midwest-numerical-analysis-day-2022/
LOCATION:East Hall\, 530 Church St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Conference,Featured Events
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DTSTART;TZID=America/Detroit:20171110T150000
DTEND;TZID=America/Detroit:20171110T160000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
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SUMMARY:MICDE Seminar: Chris Rycroft\, Department of Applied Mathematics\, Harvard University
DESCRIPTION:Bio: Chris Rycroft is an Assistant Professor of Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University. From 2010–2013\, he was a Morrey Assistant Professor in the UC Berkeley Mathematics Department\, and he was involved in the Bay Area Physical Sciences-Oncology where he collaborated with several experimental groups at Berkeley and UC San Francisco\, on using computational modeling to understand the role of mechanical forces between cells and their environment. Prof. Rycroft’s research focuses on mathematical modeling and scientific computation\, particularly for interdisciplinary applications in science and engineering. He works on a variety of problems\, and has collaborated in a number of fields including physics\, biology\, materials science\, and mechanical engineering. His current interests include questions that relate to the mechanics of materials\, numerical algorithms\, and geometry. Several of his recent projects relate to energy production and efficiency\, such as modeling bulk metallic glasses\, and developing high-throughput screening techniques to find advanced materials for carbon capture applications. He has also released several software libraries\, including Voro++ for three-dimensional computations of the Voronoi tessellation. \nThe reference map technique for simulating complex materials and multi-body interactions\nConventional computational methods often create a dilemma for fluid-structure interaction problems. Typically\, solids are simulated using a Lagrangian approach with grid that moves with the material\, whereas fluids are simulated using an Eulerian approach with a fixed spatial grid\, requiring some type of interfacial coupling between the two different perspectives. Here\, a fully Eulerian method for simulating structures immersed in a fluid will be presented. By introducing a reference map variable to model finite-deformation constitutive relations in the structures on the same grid as the fluid\, the interfacial coupling problem is highly simplified. The method is particularly well suited for simulating soft\, highly-deformable materials and many-body contact problems\, and several examples from engineering and biology will be presented. This is joint work with Ken Kamrin (MIT). \nThis is a joint seminar with the Interdisciplinary Applied Mathematics seminar series. \nProf. Rycroft is being hosted by Prof. Alben (Mathematics). If you would like to meet him please email Prof. Alben at alben@umich.edu or Dr. Mariana Carrasco-Teja at mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-chris-rycroft-department-of-applied-mathematics-harvard-university/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=UTC:20160913T161000
DTEND;TZID=UTC:20160913T170000
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SUMMARY:MICDE Seminar: Nathan Kutz\, University of Washington
DESCRIPTION:Bio: Nathan Kutz is the Robert Bolles and Yasuko Endo Professor in the department of Applied Mathematics\, and an adjunct professor of Electrical Engineering and Physics at the University of Washington. He was awarded the B.S. in Physics and Mathematics from the University of Washington in 1990 and the PhD in Applied Mathematics from Northwestern University in 1994. Following postdoctoral fellowships at the Institute for Mathematics and its Applications (University of Minnesota\, 1994-1995) and Princeton University (1995-1997)\, he joined the faculty of applied mathematics and served as Chair from 2007-2015. \nData-driven discovery of dynamical systems in the engineering\, physical and biological sciences\nWe demonstrate that the integration of data-driven dynamical systems and machine learning strategies with adaptive control are capable of producing efficient and optimal self-tuning algorithms for many complex systems arising in the engineering\, physical and biological sciences. We demonstrate that we can use emerging\, large-scale time-series data from modern sensors to directly construct\, in an adaptive manner\, governing equations\, even nonlinear dynamics\, that best model the system measured using sparsity-promoting techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of example problems. Ultimately\, the method can be used to construct adaptive controllers which are capable of obtaining and maintaining optimal states while the machine learning and sparse sensing techniques characterize the system itself for rapid state identification and improved optimization. \nThis seminar is co-sponsored by the U-M Department of Mathematics.
URL:https://micde.umich.edu/event/micde-2016-fall-seminar-series-nathan-kutz-university-of-washington/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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