BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Michigan Institute for Computational Discovery and Engineering - ECPv6.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://micde.umich.edu
X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20170312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20171105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20221116T153000
DTEND;TZID=America/Detroit:20221116T163000
DTSTAMP:20230713T163322Z
CREATED:20220825T193358Z
LAST-MODIFIED:20230713T163322Z
UID:10000579-1668612600-1668616200@micde.umich.edu
SUMMARY:MICDE Seminar: Miguel Bessa Associate Professor of Engineering\, Brown University
DESCRIPTION:Miguel Bessa is an Associate Professor in the School of Engineering at Brown University. His research interests include computational mechanics and materials science\, development of numerical methods\, machine learning and optimization\, multi-scale modeling of materials and structures. Miguel Bessa and his research group envision a new era for the design of materials and structures using artificial intelligence. Miguel received a PhD in Mechanical Engineering from Northwestern University in 2016 as a Fulbright scholar. After a short postdoctoral position at Caltech (2017) and a quick leap from Assistant to Associate Professor (2021) at Delft University of Technology\, he joined the Solid Mechanics Group at Brown University in the Summer of 2022. \nCOOPERATIVE DATA-DRIVEN MODELING \nThe human brain is capable of learning tasks mostly without forgetting. However\, deep neural networks suffer from catastrophic forgetting when learning tasks one after the other. We address this challenge considering a class-incremental learning scenario where the network sees test data without knowing its origin. We show the best results to date for the ImageNet dataset\, outperforming by more than 20% the state of the art. The proposed method is also applied to learn material laws\, illustrating its versatility. This strategy is believed to open new avenues for cooperation among different researchers and practitioners. \n  \n\nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Bessa will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu \nWATCH THE RECORDING HERE.
URL:https://micde.umich.edu/event/micde-seminar-miguel-bessa-associate-professor-of-engineering-brown-university/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/08/Miguel-bessa.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20221031T153000
DTEND;TZID=America/Detroit:20221031T163000
DTSTAMP:20230905T171445Z
CREATED:20230905T171445Z
LAST-MODIFIED:20230905T171445Z
UID:10000581-1667230200-1667233800@micde.umich.edu
SUMMARY:MICDE Seminar: Reese Jones\, Distinguished Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:Reese Jones is currently a staff scientist at Sandia National Laboratories in Livermore\, CA. He is engaged in materials science and computational physics research with scales ranging from atomic/molecular to the continuum. He has made contributions to multiscale methods\, electrochemical and thermal transport\, atomic-level fracture\, and contact. Recently he has been developing and applying machine learning methods to provide constitutive models for\ncomplex materials\, quantify material uncertainty\, and interpret materials imaging for reliability analysis. \nPREDICTING FAILURE IN POROUS METALS USING CONVOLUTIONAL NEURAL NETWORKS \nPredicting whether defects are critical or not is a high-value task in medicine\, materials engineering\, and other fields. Tools that augment expert opinion are needed in the current era of high resolution imaging that can reveal an overwhelming number of defects. In particular\, porosity is a persistent feature of additively manufactured materials and determines failure locations through complex mechanics that exhibit sensitivity to the initial pore locations. In the case of materials engineering expensive direct numerical simulations are available and can be used to train efficient surrogates. Neural networks\, such as the one we have developed\, enable more complete analysis of potential outcomes. \nIn this work\, we develop convolutional neural networks as surrogate models for predicting failure\nlocations. The binary classification problem of categorizing intact/failed voxels is first regularized by recasting it as a regression problem for the continuous damage field subjected to pre-processing transformations. An apparent challenge is the damage fields display a relatively small number of voxels close to failure leading to a form of class imbalance for regression that can cause the optimizer to converge to a poor local minimum. We address this through a re-weighting of the loss function which accounts for the relative frequencies of damage values. Another challenging aspect is the high sensitivity of the outcomes to the porosity field which typically creates multiple regions of high damage competing for failure. This motivates the use of Bayesian neural networks to capture sensitivities in the prediction through uncertainty quantification. We use these uncertainties to rank the likelihood of failure of any particular cluster of porosity in a reliability analysis. Lastly\, to aid transferability of the network and reduce the training burden when it is applied to new materials and processes\, we are exploring transfer learning techniques. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Jones will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-reese-jones-scientist-sandia-national-laboratories/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Reese-Jones.jpg
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20221031T153000
DTEND;TZID=America/Detroit:20221031T163000
DTSTAMP:20230714T153416Z
CREATED:20230714T153416Z
LAST-MODIFIED:20230714T153416Z
UID:10000603-1667230200-1667233800@micde.umich.edu
SUMMARY:MICDE Seminar: Reese Jones\, Distinguished Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:Reese Jones is currently a staff scientist at Sandia National Laboratories in Livermore\, CA. He is engaged in materials science and computational physics research with scales ranging from atomic/molecular to the continuum. He has made contributions to multi-scale methods\, electrochemical and thermal transport\, atomic-level fracture\, and contact. Recently he has been developing and applying machine learning methods to provide constitutive models for\ncomplex materials\, quantify material uncertainty\, and interpret materials imaging for reliability analysis. \nPREDICTING FAILURE IN POROUS METALS USING CONVOLUTIONAL NEURAL NETWORKS \nPredicting whether defects are critical or not is a high-value task in medicine\, materials engineering\, and other fields. Tools that augment expert opinion are needed in the current era of high resolution imaging that can reveal an overwhelming number of defects. In particular\, porosity is a persistent feature of additively manufactured materials and determines failure locations through complex mechanics that exhibit sensitivity to the initial pore locations. In the case of materials engineering expensive direct numerical simulations are available and can be used to train efficient surrogates. Neural networks\, such as the one we have developed\, enable more complete analysis of potential outcomes. \nIn this work\, we develop convolutional neural networks as surrogate models for predicting failure\nlocations. The binary classification problem of categorizing intact/failed voxels is first regularized by recasting it as a regression problem for the continuous damage field subjected to pre-processing transformations. An apparent challenge is the damage fields display a relatively small number of voxels close to failure leading to a form of class imbalance for regression that can cause the optimizer to converge to a poor local minimum. We address this through a re-weighting of the loss function which accounts for the relative frequencies of damage values. Another challenging aspect is the high sensitivity of the outcomes to the porosity field which typically creates multiple regions of high damage competing for failure. This motivates the use of Bayesian neural networks to capture sensitivities in the prediction through uncertainty quantification. We use these uncertainties to rank the likelihood of failure of any particular cluster of porosity in a reliability analysis. Lastly\, to aid transferability of the network and reduce the training burden when it is applied to new materials and processes\, we are exploring transfer learning techniques. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Jones will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-reese-jones-distinguished-member-of-the-technical-staff-sandia-national-laboratories/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/07/Reese-Jones.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20191209T150000
DTEND;TZID=America/Detroit:20191209T160000
DTSTAMP:20230905T171338Z
CREATED:20230905T171338Z
LAST-MODIFIED:20230905T171338Z
UID:10000250-1575903600-1575907200@micde.umich.edu
SUMMARY:MICDE Seminar: Bo Zhu\, Assistant Professor\, Computer Science\, Dartmouth College
DESCRIPTION:Bio: Bo Zhu is an assistant professor of Computer Science at Dartmouth College. Prior to that\, he was a postdoctoral associate at MIT CSAIL. He received his Ph.D. in Computer Science from Stanford University in 2015. His research interests encompass computer graphics\, computational physics\, and computational fabrication. In particular\, he focuses on building computational approaches to automate the process of exploring complex physical systems. \nSuper-Resolution Structural Simulation and Optimization\nComplex physical systems exhibiting mixed-dimensional geometry and multi-scale mechanics are ubiquitous. Examples include biological structures\, such as insect wing exoskeletons\, fluid phenomena\, such as bubbles and jets\, and human-made objects\, such as microrobots. The beauty and complexity of these systems attract efforts from scientists\, engineers\, and artists in various fields. However\, a computational investigation of these systems on the level of super-resolution  –with millions to billions of computational elements — is still challenging\, due to the non-manifold geometric structures\, non-linear governing physics\, and the tight coupling between them. \nMy work tackles these challenges by rethinking of the computation pipeline—from a perspective that aims to blur the line between discrete geometry and continuous physics. My guiding principle is to study the hidden low-dimensional topological and structural characteristics underpinning these complex systems and to create the most natural geometric analogs in a discrete setting for efficient simulation and optimization. In this talk\, I will present two examples to demonstrate this methodology\, including a super-resolution topology optimization algorithm based on sparse grids to emerge biomimetic structures and a numerical simulation approach based on simplicial complexes to model codimensional fluids. These computational tools enable the investigation\, discovery\, and development of a broad range of complex physical systems that are multi-scale and mixed-dimensional\, with applications in computer graphics\, computational physics\, and additive manufacturing. \n  \nProf. Zhu is being hosted by Prof. Saitou (ME).  If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE graduate student and would like to join Prof. Zhu for lunch please RSVP by Friday\, December 6th .  \n 
URL:https://micde.umich.edu/event/fall2019-zhu-dartmouth/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2019/07/Bo-Zhu.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20191106T150000
DTEND;TZID=America/Detroit:20191106T160000
DTSTAMP:20230905T171337Z
CREATED:20230905T171337Z
LAST-MODIFIED:20230905T171337Z
UID:10000292-1573052400-1573056000@micde.umich.edu
SUMMARY:MICDE Seminar: Pablo Zavattieri\, Professor\, Civil Engineering\, Purdue University
DESCRIPTION:Bio: Dr. Pablo Zavattieri is a Professor of Civil Engineering and University Faculty Scholar at Purdue University. Zavattieri received his BS/MS degrees in Nuclear Engineering from the Balseiro Institute (Argentina) and PhD in Aeronautics and Astronautics Engineering from Purdue University. He worked at the General Motors Research and Development Center as a staff researcher for 9 years\, where he led research activities in the general areas of computational solid mechanics\, smart and biomimetic materials. His current research lies at the interface between solid mechanics and materials engineering. He has focused on the fundamental aspects of how Nature uses elegant and efficient ways to make remarkable materials and their translation to engineering materials. He has contributed to the area of biomimetic materials by investigating the structure-function relationship of naturally-occurring high-performance materials at multiple length-scales\, combining state-of-the-art computational techniques and experiments to characterize the properties.   \nCLEVER ARCHITECTURES\, INTERFACES AND COMPETING MECHANISMS IN BIOLOGICAL MATERIALS\nNature uses modest constituents to synthesize composite materials with exceptional mechanical properties for structural and impact resistance purposes. In most cases\, these materials achieved outstanding mechanical properties avoiding the typical trade-offs often attained by manmade materials. While these materials require modern microscopy techniques to characterize their complex hierarchical structures\, most of our learnings come from the way these materials mitigate catastrophic damage\, revealing the most important mechanisms and features of their inner structure that contribute to energy dissipation and toughening. Considering the current progress in material synthesis and manufacturing\, these new concepts have converged to the field of architected materials.  In this talk\, I will describe some interesting mechanics problems that we encountered as we studied some extraordinary species\, and how we can translate these lessons learned to architected materials. In particular\, I will focus on a few examples related to how the combination of clever architectures\, interfaces\, material properties and competing mechanisms can promote delocalization to mitigate catastrophic failure\, hence\, improving toughness and impact resistance without sacrificing other important mechanical properties. Most of this discussion is driven by how we can eventually translate these lessons learned to the development and manufacturing of architected materials. \nProf. Zavattieri is being hosted by Prof. Evgueni Flipov (CEE). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE or CEE student and would like to join Prof. Zavattieri for lunch please RSVP by Monday\, November 4th. 
URL:https://micde.umich.edu/event/micde-seminar-pablo-zavattieri-professor-civil-engineering-purdue-university/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2019/10/Pablo-Zavattieri.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20190925T150000
DTEND;TZID=America/Detroit:20190925T160000
DTSTAMP:20230905T171404Z
CREATED:20230905T171404Z
LAST-MODIFIED:20230905T171404Z
UID:10000244-1569423600-1569427200@micde.umich.edu
SUMMARY:MICDE Seminar: H. Metin Aktulga\, Assistant Professor\, Computer Science and Engineering\, Michigan State University
DESCRIPTION:Bio: H. Metin Aktulga received his B.S. degree from Bilkent University in 2004\, M.S. and Ph.D. degrees from Purdue University in 2009 and 2010\, respectively; all in Computer Science. Before joining the Michigan State University (MSU) in 2014\, he was a postdoctoral researcher in the Computational Research Division at the Lawrence Berkeley Lab. He directs the Scalable Parallel Technologies and Algorithms (SParTA) Lab at MSU. Research in the SParTA Lab focuses on HPC and applications of HPC\, specifically on the design and development of algorithms\, numerical methods and software systems that can harness the full potential of state-of-the-art computing platforms to address challenging problems in large scale scientific computations and big-data analytics problems. Dr. Aktulga’s research is supported by NSF\, DOE\, AFRL\, NIH and the MSU Foundation. He is the recipient of the NSF CAREER award in 2019. \nTowards Fast\, Scalable and High Fidelity Reactive Molecular Dynamics Simulations\nReactive molecular dynamics (RMD) models bridge quantum-scale and classical MD approaches by explicitly modeling bond activity and redistribution of charges. As such they enable the study of important phenomena which otherwise is impractical using classical or quantum techniques. However\, RMD models have a significantly complex formulation\, making fast\, scalable and high fidelity RMD simulations extremely challenging to achieve. In this talk\, I will present our work towards addressing both the scalability and fidelity challenges. I will start by describing the parallel algorithms and numerical techniques that we developed for a fast implementation of the Reax Force Field (ReaxFF)\, which is used by hundreds of researchers worldwide. Particular emphasis will be on novel solvers we recently developed for the dynamic charge distribution problem that constitutes the most important scalability bottleneck in large RMD simulations. I will conclude the talk by outlining our efforts towards addressing the fidelity challenge\, i) through an automated force field framework for RMD models\, ii) by developing a novel hybrid ReaxFF/AMBER simulation software in the spirit of QM/MM techniques. \nProf. Aktulga is being hosted by the Glotzer Lab (Chemical Engineering). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE students\, or a Chemical Engineering graduate student\, and would like to join Prof. Aktulga for lunch\, please RSVP here by September 23rd.  \n 
URL:https://micde.umich.edu/event/fall2019-aktulga-msu/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2019/07/Metin-Aktulga.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20180330T140000
DTEND;TZID=America/Detroit:20180330T150000
DTSTAMP:20230905T171419Z
CREATED:20230905T171419Z
LAST-MODIFIED:20230905T171419Z
UID:10000125-1522418400-1522422000@micde.umich.edu
SUMMARY:MICDE Seminar: Michael Falk\, Department of Materials Science and Engineering\, Johns Hopkins University
DESCRIPTION:Bio: Michael Falk is a professor of Materials Science and Engineering at Johns Hopkins University where he also serves as the Vice Dean for Undergraduate Education. He holds a bachelor’s degree in physics and a master’s degree in Computer Science from Johns Hopkins. He completed his Ph.D. in physics at the University of California\, Santa Barbara and then launched his academic career as a computational materials scientist at the University of Michigan in 2000. In 2008 he returned to Johns Hopkins as an associate professor of Materials Science and Engineering with joint appointments in Mechanical Engineering and Physics. Prof. Falk’s research focuses on utilizing computer simulation on the atomic scale to understand the processes by which materials are pushed out of equilibrium by processes such as bending\, breaking\, charging and undergoing frictional sliding. His research has had an abiding focus on the ways glass structures accommodate plastic flow\, deformation and fracture. These investigations have involved developing new methodologies for deploying molecular dynamics simulations and the development of thermodynamically motivated constitutive theories. Prof. Falk also engages in educational research and is a strong advocate for diversity and inclusion\, engaging in outreach to Baltimore City elementary schools and advocating for a welcoming climate for LGBTQ people within the engineering and physics professions. \nConnecting atomistic simulations\, defect-based theories and continuum plasticity in amorphous solids\nGlasses\, and the more general category of materials known as amorphous solids\, lack crystal structure and find wide application from consumer goods to photovoltaics. Yet\, issues quantifying disorder have stymied the construction of physically grounded mechanical constitutive laws for these materials suitable for failure prediction. Atomistic simulation methods can provide some insight regarding the mechanisms of plastic deformation and strain localization. Recent investigations have aimed at quantifying the defects that control plastic flow by quantifying a yield stress field at the nanometer scale. Analysis of these fields have confirmed some of the assumptions built into the shear transformation zone theory of amorphous plasticity\, most notably the orientational nature of the defect and their pre-existence in the structure. I will further discuss methods for quantitatively predicting strain localization\, a limiting failure process in high-strength metallic glasses and other amorphous materials by parameterizing the effective-temperature shear transformation zone theory from molecular dynamics simulations. We have directly cross-compared molecular dynamics simulations and continuum representations of these same materials in order to test and validate our constitutive theories. The role of coarse graining in the linkage of continuum and atomistic methods is crucial\, and convergence only arises above a critical length scale on the order of tens of angstroms. The investigation makes clear the need to separate out the relevant fluctuations in material structure from the shorter wavelength fluctuations that serve to obscure them. It is\, in the end\, the interactions between these larger-scale relevant fluctuations via the material’s mechanical response that controls the failure process during strain localization. \nProf. Falk is being hosted by Prof. Yue Fan (Mechanical Engineering). If you would like to meet him during his visit please email micde-contact@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-michael-falk-hopkins/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2018/01/Michael-Falk.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1303 EECS 1301 Beal Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave:geo:-83.713272,42.292322
END:VEVENT
END:VCALENDAR