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X-WR-CALDESC:Events for Computational Science
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230203T153000
DTEND;TZID=UTC:20230203T163000
DTSTAMP:20260421T185353
CREATED:20221116T173553Z
LAST-MODIFIED:20221116T173553Z
UID:795-1675438200-1675441800@computationalscience.uci.edu
SUMMARY:CSRC Colloquium - Youngsoo Choi
DESCRIPTION:Title:  Physics-constrained data-driven methods for accurately accelerating simulations \nAbstract:  A data-driven model can be built to accurately accelerate computationally expensive physical simulations\, which is essential in multi-query problems\, such as inverse problem\, uncertainty quantification\, design optimization\, and optimal control. In this talk\, two types of data-driven model order reduction techniques will be discussed\, i.e.\, the black-box approach that incorporates only data and the physics-constrained approach that incorporates the first principle as well as data. The advantages and disadvantages of each method will be discussed. Several recent developments of generalizable and robust data-driven physics-constrained reduced order models will be demonstrated for various physical simulations as well. For example\, a hyper-reduced time-windowing reduced order model overcomes the difficulty of advection-dominated shock propagation phenomenon\, achieving a speed-up of O(20~100) with a relative error much less than 1% for Lagrangian hydrodynamics problems\, such as 3D Sedov blast problem\, 3D triple point problem\, 3D Taylor Green vortex problem\, 2D Gresho vortex problem\, and 2D Rayleigh Taylor instability problem. The nonlinear manifold reduced order model also overcomes the challenges posed by the problems with Kolmogorov’s width decaying slowly by representing the solution field with a compact neural network decoder\, i.e.\, nonlinear manifold. The space time reduced order model accelerates a large-scale particle Boltzmann transport simulation by a factor of 2\,700 with a relative error less than 1%. Furthermore\, successful application of these reduced order models for mate-material lattice structure design optimization problems will be presented. Finally\, the library for reduced order modeling tool\, i.e.\, libROM (https://www.librom.net)\, and its webpage and several YouTube tutorial videos will be introduced. \nSpeaker:  Youngsoo Choi\, Research Scientist\, Lawrence Livermore National Laboratory \nBio: Youngsoo is a research scientist in Center for Applied Scientific Computing at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems\, such as inverse problems\, design optimization\, and uncertainty quantification. His expertise includes various scientific computing disciplines. He has also developed the component-wise reduced order model optimization algorithm\, which enables fast and accurate computational modeling tool for lattice-structure design. He is currently leading data-driven surrogate modeling development team for various physical simulations\, with whom he developed an open source code\, libROM. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc at Sandia National Laboratories and Stanford University prior to joining LLNL in 2017. \nPresenter Website: https://people.llnl.gov/choi15\nDepartment Link: https://computing.llnl.gov/casc \n\nHost:  Jose Castillo (CSRC) and the Sustainable Horizons Institute CRLC Virtual Seminar Series
URL:https://computationalscience.uci.edu/event/csrc-colloquium-youngsoo-choi/
LOCATION:Virtual Zoom Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230303T153000
DTEND;TZID=UTC:20230303T163000
DTSTAMP:20260421T185353
CREATED:20221116T173958Z
LAST-MODIFIED:20221116T173958Z
UID:797-1677857400-1677861000@computationalscience.uci.edu
SUMMARY:CSRC Colloquium - Sven Leffer
DESCRIPTION:Title:  Nonlinear Optimization at the National Labs \nAbstract:  We review a range of challenging DOE applications in design and control that are formulated as nonlinear optimization problems\, ranging from the modeling of gas and electricity networks\, over the design of new materials\, to the analysis of complex data. We show how research into new algorithms and solution techniques enables DOE scientists to solve these applications. \nSpeaker:  Sven Leffer\, Mathematics and Computer Science\, Argonne National Laboratory \nBio:  Sven Leyffer joined the Mathematics and Computer Science Division at Argonne in 2002\, where he is now a senior computational mathematician. Sven is a SIAM Fellow\, and a senior fellow of the University of Chicago/Argonne Computation Institute. Leyffer obtained his Ph.D. in 1994 from the University of Dundee\, Scotland\, and held postdoctoral research positions at Dundee\, Argonne\, and Northwestern University. \nPresenter Website: https://wiki.mcs.anl.gov/leyffer/ \nHost: Jose Castillo (CSRC) and the Sustainable Horizons Institute CRLC Virtual Seminar Series
URL:https://computationalscience.uci.edu/event/csrc-colloquium-sven-leffer/
LOCATION:Virtual Zoom Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230407T153000
DTEND;TZID=UTC:20230407T163000
DTSTAMP:20260421T185353
CREATED:20221116T174058Z
LAST-MODIFIED:20221116T174058Z
UID:799-1680881400-1680885000@computationalscience.uci.edu
SUMMARY:CSRC Colloquium - Prasanna Balaprakash
DESCRIPTION:Title:  Democratizing Deep Learning Development with DeepHyper \nAbstract:  In recent years\, deep neural networks (DNNs) have achieved considerable success in learning complex nonlinear relationships between features and targets from large datasets. Nevertheless\, designing high-performing DNN architecture for a given data set is an expert-driven\, time-consuming\, trial-and-error manual task. A major bottleneck in the construction of DNNs is the vast search space of architectures that need to be explored in the face of new data sets. Moreover\, DNNs typically require user-specified values for hyperparameters\, which strongly influence performance factors such as training time and prediction accuracy. In this talk\, we will introduce participants to DeepHyper\, a scalable automated machine learning package for developing deep neural networks. DeepHyper provides an infrastructure that targets experimental research in neural architecture search (NAS) and hyperparameter search (HPS) methods\, scalability\, and portability across diverse supercomputers through the use of the different workflow manager. \nSpeaker: Prasanna Balaprakash\, Computer Scientist\, Argonne National Laboratory \nBio: Prasanna Balaprakash is a computer scientist with a joint appointment in the Mathematics and Computer Science Division and the Leadership Computing Facility at Argonne National Laboratory. His research work spans the areas of artificial intelligence\, machine learning\, optimization\, and high-performance computing. He is a recipient of the U.S. Department of Energy 2018 Early Career Award. Prior to Argonne\, he worked as a Chief Technology Officer at Mentis Sprl\, a machine learning startup in Brussels\, Belgium. He received his PhD from CoDE-IRIDIA (AI Lab)\, Università Libre de Bruxelles\, Brussels\, Belgium\, where he was a recipient of European Commissions Marie Curie and Belgian F.R.S-FNRS Aspirant fellowships. \nPresenter Website: http://pbalapra.github.io/\nDepartment Link: https://deephyper.readthedocs.io/en/latest/
URL:https://computationalscience.uci.edu/event/csrc-colloquium-prasanna-balaprakash/
LOCATION:Virtual Zoom Seminar
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