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DTSTART;TZID=America/Detroit:20231005T140000
DTEND;TZID=America/Detroit:20231005T150000
DTSTAMP:20260605T080346
CREATED:20230915T193609Z
LAST-MODIFIED:20231004T170032Z
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SUMMARY:LANL XPS Seminar: Mark W. Schraad\, Division Leader for Computational Physics at Los Alamos National Laboratory
DESCRIPTION:Bio: Mark W. Schraad earned his Ph.D. in Aerospace Engineering from the University of Michigan and has nearly three decades of research and development experience at Los Alamos National Laboratory. He focused his research career on materials physics\, with specialization in structured materials and material instabilities\, while also gaining scientific leadership experience across theoretical and computational physics\, modeling and simulation\, and scientific software development for advanced computing architectures and hardware. Mark has balanced experience across Los Alamos Science\, Technology\, and Engineering and Weapons Directorates\, and across the Laboratory’s basic science and mission application portfolios. In his current position\, he serves as Division Leader for Computational Physics within the Weapons Physics Directorate at Los Alamos National Laboratory. In this role\, he is responsible for the development and delivery of LANL’s suite of mission-critical modeling and simulation software\, which is used in the design\, certification\, and assessment of the U.S. nuclear stockpile. \nHigh-Performance Computing and the Future of Big Science for Department of Energy Applications\nLos Alamos is the birthplace of computational physics and has been at the forefront of high-performance computing for nearly eight decades. Integrating physics theory and advanced numerical methods in the instantiation of multi-physics software has allowed Los Alamos to address a broad range of science and technology applications. Today\, as one of 17 Department of Energy National Laboratories\, Los Alamos continues to develop and deploy advanced software in the execution of a complex mission across national security\, energy security\, and environmental and climate science. As part of that endeavor\, the Computational Physics Division at Los Alamos develops and delivers a continuously evolving suite of production software products to design and analyze large-scale integrated physics experiments and to enable the design\, assessment\, and confident certification of the U.S. nuclear stockpile. These software products are deployed on leading-edge\, high-performance computing platforms\, such as the Trinity and Crossroads supercomputers at Los Alamos\, and the Sierra and El Capitan machines at Lawrence Livermore National Laboratory. With a shifting geopolitical landscape\, our software serves a national security mission of ever-increasing importance. Yet\, simultaneously\, the rapid pace of science and technology change places a premium on agility\, with a diversity of computing platforms and architectures coming online\, and with AI poised to revolutionize approaches to modern science. Ultimately\, an integration of artificial intelligence methodologies with the co-design of software and future computing architectures will allow new levels of physics fidelity\, numerical accuracy\, and efficiency in time to solution for the most challenging scientific workflows to address a broad spectrum of future\, big-science problems. \n  \nSnacks and refreshments will be provided!
URL:https://micde.umich.edu/event/lanl-xps-seminar-mark-w-schraad-division-leader-los-alamos-national-laboratory/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde Seminar,MICDE Seminar Series,Scientific Computing
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231010T130000
DTEND;TZID=America/Detroit:20231010T170000
DTSTAMP:20260605T080346
CREATED:20230817T202937Z
LAST-MODIFIED:20231009T151118Z
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SUMMARY:MICDE ACES Mini-Symposium: MICDE Catalyst Grant Showcase
DESCRIPTION:MICDE is excited to present the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium. ACES is a highly anticipated event aiming to highlight current trends and hot topics in computational science\, machine learning and AI. This event showcases the outstanding research supported by the MICDE Catalyst Grant and provides a unique opportunity to share your ideas and network with fellow faculty members during a networking reception. \nThis year’s mini-symposium features the following exceptional faculty: \n\n\n\n\n\n\n\n\nGary Luker\, Professor | Department of Radiology | Department of Biomedical Engineering | Department of Microbiology and Immunology\nSalar Fattahi\, Assistant Professor | Department of Industrial and Operations Engineering\nJesse Capecelatro\, Associate Professor | Department of Mechanical Engineering | Department of Aerospace Engineering\n\n\n\n\n\n\n\nVenkat Viswanathan\, Associate Professor | Department of Aerospace Engineering\nGeorge Tzimpragos\, Assistant Professor | Department of Computer Science and Engineering\nGokul Ravi\, Assistant Professor | Department of Electrical Engineering and Computer Science\n\n\n\n  \nSchedule: \n\n\n\nSpeaker\nDepartment \nTime\nTitle\n\n\nVancho Kocevski\nMICDE\n1:00 pm\nMICDE Welcome\n\n\n\n\n1:05 pm\nFelicitation of Krishna Garikipati for his contributions to Computational Science & Engineering @ UM\n\n\nGary Luker\nRadiology | Biomedical Engineering | Microbiology and Immunology\n1:30 pm\nA physics-constrained AI framework for cancer cell migration\n\n\nSalar Fattahi\nIndustrial and Operations Engineering\n1:55 pm\nScalable Inference of Dynamic Graphical Models with Combinatorial Structures\n\n\nBreak\n \n2:20 pm\n\n\n\nJesse Capecelatro\nMechanical Engineering | Aerospace Engineering\n2:30 pm\nParticle-laden flows: simulations and data-driven modeling\n\n\nVenkat Viswanathan\nAerospace Engineering\n2:55 pm\nAI Foundation models for materials science\n\n\nBreak\n \n3:20 pm\n\n\n\nGeorge Tzimpragos\nElectrical Engineering and Computer Science \n3:30 pm\nBeating Resistance\n\n\nGokul Ravi\nElectrical Engineering and Computer Science \n3:55 pm\nA Hybrid Quantum-Classical Computing Ecosystem\n\n\nNetworking Session\n \n4:30 pm\n\n\n\nAdjourn\n \n5:15 pm\n\n\n\n\n  \nThe event finishes with a networking reception\, where faculty members can share their research ideas and future plans\, sparking collaborations.
URL:https://micde.umich.edu/event/conference-symposiummicde-aces-mini-symposium/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor
CATEGORIES:Aces,Computation,Computational Modeling,Computational Science,Computational Social Science,computing,Research,Science,Scientific Computing,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231012T110000
DTEND;TZID=America/Detroit:20231012T130000
DTSTAMP:20260605T080346
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:20231019T110000
DTEND;TZID=America/Detroit:20231019T130000
DTSTAMP:20260605T080346
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:20231026T110000
DTEND;TZID=America/Detroit:20231026T130000
DTSTAMP:20260605T080346
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
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