{"id":34,"date":"2017-10-16T16:50:52","date_gmt":"2017-10-16T20:50:52","guid":{"rendered":"https:\/\/www.ce.jhu.edu\/lori-new\/?page_id=34"},"modified":"2023-02-12T10:42:10","modified_gmt":"2023-02-12T15:42:10","slug":"home","status":"publish","type":"page","link":"https:\/\/www.ce.jhu.edu\/lori\/","title":{"rendered":""},"content":{"rendered":"<p><em>The Graham-Brady research group focuses on the problem of modeling the effects that material randomness has on structural performance. This material variability plays a key role in localizations that lead to large-scale structural failure. Our approaches include stochastic modeling, deep learning, transfer learning, and uncertainty quantification. Recent application areas include ceramics and concrete under high-rate loading, characterization error in high-strength metal alloys, composite materials under impact, and lightweight porous materials.<\/em><\/p>\n<p style=\"text-align: center;\"><div id=\"metaslider-id-29\" style=\"max-width: 900px;\" class=\"ml-slider-3-110-0 metaslider metaslider-responsive metaslider-29 ml-slider .metaslider .nivo-caption {     right: 0px;     bottom: 0px;     width: 20%;     height: 100%;     left: auto; } has-dots-nav ms-theme-nivo-bar\" role=\"region\" aria-label=\"New Slider\" data-height=\"400\" data-width=\"900\">\n    <div id=\"metaslider_container_29\">\n        <ul id='metaslider_29' class='rslides'>\n            <li aria-roledescription='slide' aria-labelledby='slide-0'><a href=\"http:\/\/machconference.org\" target=\"_self\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.ce.jhu.edu\/lori\/wp-content\/uploads\/2023\/02\/Picture1-scaled-900x400.jpg\" height=\"400\" width=\"900\" alt=\"\" class=\"slider-29 slide-31 msDefaultImage\" \/><div class=\"caption-wrap\"><div class=\"caption\">Microstructural heterogeneity affects the macro-scale behavior of materials. Conversely, loading at the macro-scale changes the microstructural response. These up-scaling and down-scaling relations are often modeled using multiscale finite element (FE) approaches such as FE-squared (FE2). However, FE2 requires numerous calculations at the micro-scale, which often renders this approach intractable. To overcome this challenge, Ph.D. student Ashwini Gupta and postdoc Anindya Bhaduri have developed an enormously faster machine learning (ML) based approach for multiscale mechanics modeling as shown in the figure.<\/div><\/div><\/a><\/li>\n            <li style='display: none;' aria-roledescription='slide' aria-labelledby='slide-1'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.ce.jhu.edu\/lori\/wp-content\/uploads\/2018\/10\/sliderpicture2-3.png\" height=\"400\" width=\"900\" alt=\"\" class=\"slider-29 slide-132 msDefaultImage\" title=\"sliderpicture2\" \/><div class=\"caption-wrap\"><div class=\"caption\">Explicitly modeling the process of failure and fragmentation in brittle materials presents a major challenge to understanding the performance of these materials. The material undergoes a process of microcrack initiation, growth and coalescence that ultimately leads to fragmentation of the material. This transition from a damaged solid medium to a granular material happens is an unstable transition, which makes it difficult to represent these mechanisms in a larger-scale constitutive model. Former Ph.D. student Amartya Bhattacharjee and former Postdoctoral Scholar Mehmet Cil collaborated with others at JHU and at ARL to develop a constitutive model that is capable of capturing this behavior.<\/div><\/div><\/li>\n            <li style='display: none;' aria-roledescription='slide' aria-labelledby='slide-2'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.ce.jhu.edu\/lori\/wp-content\/uploads\/2018\/10\/sliderpicture4-1.png\" height=\"400\" width=\"900\" alt=\"\" class=\"slider-29 slide-68 msDefaultImage\" title=\"sliderpicture4\" \/><div class=\"caption-wrap\"><div class=\"caption\">Representations of material microstructure based on physical characterization always exhibit some degree of error that result from resolution, noise, or statistical sampling. Postdoctoral scholar Noah Wade is studying the propagation of error from serial sectioned electron backscatter diffraction (EBSD) and other grid-based sampling techniques to three-dimensional computational models of the mechanical behavior in these materials.<\/div><\/div><\/li>\n            <li style='display: none;' aria-roledescription='slide' aria-labelledby='slide-3'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.ce.jhu.edu\/lori\/wp-content\/uploads\/2020\/04\/Simulation-900x400.png\" height=\"400\" width=\"900\" alt=\"\" class=\"slider-29 slide-391 msDefaultImage\" title=\"Simulation\" \/><div class=\"caption-wrap\"><div class=\"caption\">In order to understand variability in material performance, many computational models require multiple instantiations of material microstructure. Obtaining these samples experimentally can be very costly, and it is not possible to consider the effects of varying microstructural statistics. Digitally generated microstructures - generation of microstructures that match given target statistic - provide an efficient alternative.  These figures show work by Ph.D. student Ashwini Gupta and postdocs Anindya Bhaduri and Audrey Olivier, who are using machine learning to address this challenge for 3D multi-phase material microstructures.<\/div><\/div><\/li>\n            <li style='display: none;' aria-roledescription='slide' aria-labelledby='slide-4'><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.ce.jhu.edu\/lori\/wp-content\/uploads\/2023\/03\/Picture1-900x400.png\" height=\"400\" width=\"900\" alt=\"\" class=\"slider-29 slide-988 msDefaultImage\" title=\"Picture1\" \/><div class=\"caption-wrap\"><div class=\"caption\">The Graham-Brady research group works frequently within the Hopkins Extreme Materials Institute (HEMI), specifically the Center on AI for Materials in Extreme Environments (CAIMEE). Prof. Graham-Brady is currently the Director of CAIMEE and the Associate Director of HEMI. She is leading a team developing the AI for Materials Design (AIMD) Laboratory. AIMD will be a unique capability, with high-throughput characterization and testing unified in a robotic platform and guided by ML- and AI-driven design. For more details, see the HEMI website at hemi.jhu.edu or the CAIMEE website at hemi.jhu.edu\/caimee.\n<\/div><\/div><\/li>\n        <\/ul>\n        \n    <\/div>\n<\/div><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Graham-Brady research group focuses on the problem of modeling the effects that material randomness has on structural performance. This material variability plays a key role in localizations that lead to large-scale structural failure. Our approaches include stochastic modeling, deep learning, transfer learning, and uncertainty quantification. Recent application areas include&#8230;<\/p>\n<p class=\"continue-reading-button\"> <a class=\"continue-reading-link\" href=\"https:\/\/www.ce.jhu.edu\/lori\/\">Continue reading<i class=\"crycon-right-dir\"><\/i><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"templates\/template-onecolumn.php","meta":{"footnotes":""},"class_list":["post-34","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/pages\/34","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/comments?post=34"}],"version-history":[{"count":38,"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/pages\/34\/revisions"}],"predecessor-version":[{"id":1062,"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/pages\/34\/revisions\/1062"}],"wp:attachment":[{"href":"https:\/\/www.ce.jhu.edu\/lori\/wp-json\/wp\/v2\/media?parent=34"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}