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DTSTART;TZID=America/Detroit:20250328T120000
DTEND;TZID=America/Detroit:20250328T130000
DTSTAMP:20250324T152136Z
CREATED:20250324T152136Z
LAST-MODIFIED:20250324T152136Z
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SUMMARY:FSML Lecture Series - Lianghao Cao (Caltech): Derivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion
DESCRIPTION:Zoom link \nBio: Dr. Lianghao Cao is a Postdoctoral Scholar Research Associate from the Department of Computing and Mathematical Sciences at the California Institute of Technology. He obtained a B.S. in Engineering Mechanics from the University of Illinois at Urbana-Champaign and a Ph.D. in Computational Science\, Engineering\, and Mathematics from The University of Texas at Austin. His research blends mechanistic modeling\, uncertainty quantification\, and scientific machine learning to understand\, enhance\, and control the quality\, validity\, and reliability of simulation-based predictions of complex physical systems. \nDerivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion\nAbstract: This talk focuses on a derivative-informed supervised learning method for efficiently building machine learning surrogates of high-fidelity computational models\, particularly those governed by parametric partial differential equations. Unlike the conventional supervised learning method that treats the model as a black box\, our approach leverages additional model sensitivity information\, extracted via solving forward or adjoint sensitivity equations. This sensitivity information is integrated into the surrogate’s architecture and training process based on rigorous error analysis. We refer to such a surrogate construction as DINO (derivative-informed neural operator). \nDINO offers two key advantages over conventional surrogate construction. First\, it significantly improves the cost-accuracy trade-off for a wide range of models\, often by one to two orders of magnitude. Second\, it directly controls the surrogate Jacobian (Fréchet derivative) errors\, thus enhancing performance in surrogate-driven outer-loop problems that use gradient- and Hessian-based optimization algorithms. We demonstrate DINO’s capability to accelerate infinite-dimensional Bayesian inversion. First\, we show that geometric MCMC driven by DINO achieves a 2–9x speed up in asymptotically exact posterior sampling. Second\, we introduce LazyDINO\, a DINO-driven measure transport method for amortized Bayesian inversion\, which is one to two orders of magnitude more cost-efficient than competing methods.\nThis talk is based on joint work with Michael Brennan\, Joshua Chen\, Omar Ghattas\, Youssef Marzouk\, and Thomas O’Leary-Roseberry.
URL:https://micde.umich.edu/event/fsml-lecture-series-lianghao-cao-caltech-derivative-informed-operator-learning-with-applications-to-cost-efficient-bayesian-inversion/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Lianghao-Cao-Caltech.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20250311T130831Z
CREATED:20250311T130516Z
LAST-MODIFIED:20250311T130831Z
UID:10000814-1741953600-1741957200@micde.umich.edu
SUMMARY:FSML Lecture Series - Alexander Tong (Mila - Quebec AI Institute): Flow matching in cell trajectories and protein design
DESCRIPTION:Zoom link \nBio: Alexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins. \nFlow matching in cell trajectories and protein design\nAbstract: Generative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery.
URL:https://micde.umich.edu/event/fsml-lecture-series-alexander-tong-mila/
LOCATION:1642 GGBL\, 2350 HAYWARD ST\, Ann Arbor\, 48109\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Alexander-Tong.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250228T120000
DTEND;TZID=America/Detroit:20250228T130000
DTSTAMP:20250227T225237Z
CREATED:20250225T171723Z
LAST-MODIFIED:20250227T225237Z
UID:10000809-1740744000-1740747600@micde.umich.edu
SUMMARY:FSML Lecture Series - Doruk Aksoy: From Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition
DESCRIPTION:Zoom link \nBio:Doruk Aksoy is a 5th year PhD candidate in Aerospace Engineering and Scientific computing at the University of Michigan\, working under the supervision of Prof. Alex Gorodetsky. Prior to joining UM\, he studied Mechanical Engineering at Bogazici University in Istanbul Turkey. During his PhD\, he worked on developing incremental tensor decomposition algorithms to accelerate scientific machine learning through data reduction. \nFrom Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition\nAbstract: The rapid advancement of large-scale parallel computing created a surge of interest in developing high-fidelity digital twins for complex systems. However\, the computational demands for training these models are immense\, requiring vast amounts of data. As the spatial and temporal resolution of simulations increases\, even data storage becomes a critical bottleneck. This talk presents how low-rank tensor decomposition methods can be used to exploit the structure in large-scale data. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques.
URL:https://micde.umich.edu/event/fsml-lecture-series-doruk-aksoy/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Aksoy-Doruk.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250214T093000
DTEND;TZID=America/Detroit:20250214T103000
DTSTAMP:20250212T173805Z
CREATED:20250212T173700Z
LAST-MODIFIED:20250212T173805Z
UID:10000808-1739525400-1739529000@micde.umich.edu
SUMMARY:FSML Lecture Series - Ricardo Vinuesa: Identifying coherent structures and controlling turbulent flows through deep learning
DESCRIPTION:Zoom link \nBio: Dr. Ricardo Vinuesa is joining the Department of Aerospace Engineering at the University of Michigan in the Fall of 2025. He is currently an Associate Professor at the Department of Engineering Mechanics\, KTH Royal Institute of Technology in Stockholm. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain)\, and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand\, control and predict complex wall-bounded turbulent flows\, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received\, among others\, an ERC Consolidator Grant\, the TSFP Kasagi Award\, the MST Emerging Leaders Award\, the Goran Gustafsson Award for Young Researchers\, the IIT Outstanding Young Alumnus Award\, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain. \nIdentifying coherent structures and controlling turbulent flows through deep learning\nAbstract: In this work we first use explainable deep learning based on Shapley explanations to identify the most important regions for predicting the future states of a turbulent channel flow. The explainability framework (based on gradient SHAP) is applied to each grid point in the domain\, and through percolation analysis we identify coherent flow regions of high importance. These regions have around 70% overlap with the intense Reynolds-stress (Q) events in two-dimensional vertical planes. Interestingly\, these importance-based structures have high overlap with classical turbulence structures (Q events\, streaks and vortex clusters) in different wall-normal locations\, suggesting that this new framework provides a more comprehensive way to study turbulence. We also discuss the application of deep reinforcement learning (DRL) to discover active-flow-control strategies for turbulent flows\, including turbulent channels\, three-dimensional cylinders and turbulent separation bubbles. In all the cases\, the discovered DRL-based strategies significantly outperform classical flow-control approaches. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations. \n 
URL:https://micde.umich.edu/event/fsml-lecture-series-ricardo-vinuesa-identifying-coherent-structures-and-controlling-turbulent-flows-through-deep-learning/
LOCATION:2210 Lurie Engineering Center\, 1221 Beal Ave\, Ann Arbor\, MI\, 48105
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/Ricardo-Vinuesa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250131T120000
DTEND;TZID=America/Detroit:20250131T130000
DTSTAMP:20250127T193042Z
CREATED:20250127T173918Z
LAST-MODIFIED:20250127T193042Z
UID:10000799-1738324800-1738328400@micde.umich.edu
SUMMARY:FSML Lecture Series - Panos Stinis: When big neural networks are not enough: physics\, multi-fidelity and kernels
DESCRIPTION:Zoom link \nBio: Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems\, multiscale modeling\, uncertainty quantification\, and machine learning. He studied aeronautical engineering at the Technical University of Athens\, Greece. He earned his PhD in applied mathematics in 2003\, from Columbia University in New York and began his career as a postdoctoral fellow at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research. In 2008\, he became a faculty member at the Mathematics Department at the University of Minnesota. He moved to the Pacific Northwest National Laboratory in 2014\, where he is currently leading the Computational Mathematics group. \nWhen big neural networks are not enough: physics\, multi-fidelity and kernels\nAbstract: Modern machine learning has shown remarkable promise in multiple applications. However\, brute force use of neural networks\, even when they have huge numbers of trainable parameters\, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics\, exploiting multi-fidelity knowledge\, and the kernel representation of neural networks can lead to a significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas. \n 
URL:https://micde.umich.edu/event/fsml-lecture-series-panos-stinis-when-big-neural-networks-are-not-enough-physics-multi-fidelity-and-kernels/
LOCATION:2004 Lay Auto Lab
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/Panos-Stinis.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241206T120000
DTEND;TZID=America/Detroit:20241206T130000
DTSTAMP:20241204T144214Z
CREATED:20241011T181202Z
LAST-MODIFIED:20241204T144214Z
UID:10000779-1733486400-1733490000@micde.umich.edu
SUMMARY:FSML Lecture Series - Anoushka Bhutani: Foundation Model for Molecular Design
DESCRIPTION:Zoom link \nBio: Anoushka is a third-year PhD student in Prof. Venkat Viswanathan’s group at the University of Michigan. Her research interests include machine learning for materials design and electrochemical battery modeling. \nFoundation Model for Molecular Design\nAbstract: The paradigm of molecular machine learning for material screening has accelerated material development cycles\, improved efficiency\, and reduced costs. However\, current state-of-the-art molecular property prediction models still require labeled training data generated using wet-lab experiments or Density Functional Theory (DFT) calculations. Their utility is limited by the scarcity and heterogeneity of labeled materials datasets. Foundation models (FMs) offer a solution to this: these models use self-supervised pre-training strategies to leverage unlabeled datasets and learn representations of data that can be applied to downstream tasks. Large unlabeled datasets of billions of synthesizable molecules are readily available. Prior attempts to train FMs for molecular property prediction demonstrate promise; however\, equivariant geometric models trained using supervised learning are still more accurate. This can be attributed to the fact that foundation models are extremely expensive to train and can be difficult to interpret; they require huge computing budgets\, complex distributed computing techniques\, and extensive hyperparameter searches. Our work addresses these challenges on three fronts: (1) we have prototyped a scalable workflow for distributed training of molecular foundation models (2) we have trained large foundation models using this workflow which demonstrates state-of-the-art molecular property prediction capabilities across several benchmarks\, and (3) we have applied model interpretability strategies such as the attention visualization to shed insight on molecular structure relationships learn by the transformer. \n 
URL:https://micde.umich.edu/event/workshop-seminaranoushka-bhutani-foundation-model-for-molecular-design/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Computational Science,Engineering,FSML,Graduate School,Graduate Students,Michigan Engineering,Rackham,Research,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Copy-of-MICDE-2022-2023-Fellowship-Portraits.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241115T120000
DTEND;TZID=America/Detroit:20241115T130000
DTSTAMP:20260522T154425Z
CREATED:20241012T182154Z
LAST-MODIFIED:20260522T154425Z
UID:10000785-1731672000-1731675600@micde.umich.edu
SUMMARY:FSML Lecture Series - Hongfan Chen: Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process
DESCRIPTION:Zoom link \nBio: Hongfan Chen is a third-year PhD student in the Department of Mechanical Engineering at the University of Michigan. His research interests include data assimilation\, uncertainty quantification\, and machine learning applications in space weather. \nGlobal Geomagnetic Perturbation Forecasting with Quantified Uncertainty Using Deep Gaussian Process\nAbstract: Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH)\, as a proxy for Geomagnetically Induced Currents (GICs)\, is crucial for estimating the impact of geomagnetic storms and remains a topic under active investigation. The current state-of-the-practice Geospace model is computationally expensive for fine-grid global simulations while existing machine learning methods consistently tend to underestimate dBH. Additionally\, these models either lack uncertainty quantification (UQ) or provide UQ that lacks calibration. In this work\, as part of the NextGen SWMF project funded by NSF\, we develop a data-driven\, grid-free global model using deep Gaussian process (DGP)\, a Bayesian non-parametric approach that forecasts the dBH for the full surface of Earth with calibrated uncertainty. The model uses solar wind measurements and the Dst index as input\, and it is trained based on ground magnetometer station data provided by SuperMAG over the period 1995-2022. The model’s predictions are evaluated based on the Heidke skill score (HSS) for a total of 22 geomagnetic storms in 2015. We further test the model on the 2024 May 10-12 storm. The results demonstrate that our model outperforms the state-of-the-art model\, with predictions exhibiting high accuracy in mid-latitudes and high-latitude regions in the northern hemisphere. \n 
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-8/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2024/10/Hongfan_Chen.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241107T180000
DTEND;TZID=America/Detroit:20241107T190000
DTSTAMP:20241030T170447Z
CREATED:20241029T230120Z
LAST-MODIFIED:20241030T170447Z
UID:10000787-1731002400-1731006000@micde.umich.edu
SUMMARY:Taking the Next Step: Graduate Studies in Computation/AI for Science and Engineering at U-M
DESCRIPTION:PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master’s students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend. \nPlease register via the link: https://sessions.studentlife.umich.edu/p/track/12857 \nZoom option available after registering.
URL:https://micde.umich.edu/event/taking-the-next-step-2024/
LOCATION:GG Brown Laboratory – 2147
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Artificial Intelligence,Astronomy,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Science,Computational Social Science,computer science,computing,Earth And Environmental Sciences,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Epidemiology,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Michigan Engineering,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scientific Computing,Statistics,Talk,Undergraduate,Undergraduate Students,Virtual,Workshop
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Happening@UM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241101T120000
DTEND;TZID=America/Detroit:20241101T130000
DTSTAMP:20241106T181820Z
CREATED:20241012T182153Z
LAST-MODIFIED:20241106T181820Z
UID:10000784-1730462400-1730466000@micde.umich.edu
SUMMARY:FSML Lecture Series - Nicholas Galioto: Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models
DESCRIPTION:Zoom link \nBio: Nick Galioto is a second-year postdoctoral research fellow in the Department of Computational Medicine and Bioinformatics at the University of Michigan (UM). He received his PhD at UM in aerospace engineering in 2023 under the advising of Alex Gorodetsky and remained in the lab for an additional year as a postdoc. In the Gorodetsky lab\, Nick researched how to use stochastic models of dynamical systems to improve system identification. Now\, Nick works in the Rajapakse lab researching how to create data-driven models of the dynamics of cell reprogramming. \n  \nDiscovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models\n  \nAbstract: Cell reprogramming\, the transformation of a cell from one cell type to another through the introduction of exogenous transcription factors (TFs)\, is a rapidly developing research area that could lead to groundbreaking therapeutic technologies in areas such as tissue regeneration\, disease modeling\, and personalized medicine. However\, many challenges still exist that obstruct its practical viability. Discovering which TFs induce reprogramming requires a combinatorial search\, and testing a single candidate set of TFs experimentally can cost tens of thousands of dollars and take multiple months. Moreover\, even when an effective set of TFs is known\, cell conversion efficiency lies only around 5%. Faced with these challenges\, researchers have developed computational surrogate models to rapidly explore the TF search space at a fraction of the cost of wet lab experimentation. Unfortunately\, these models have seen limited success in practice due to the difficulty of capturing the complex gene-gene interactions within the cell\, most of which are still not well understood. With the recent high-profile rise of transformer-based foundation models for natural language\, researchers are now turning to the transformer to push past\, current performance limitations in a wide range of digital biology tasks\, including cell reprogramming. Of particular interest in these models is the attention mechanism\, which is potentially well-suited for capturing long-range gene-gene interactions at a higher fidelity than previously possible. In this talk\, I will describe how the transformer architecture has been adapted for cellular biology and analyze the utility of one such model\, Geneformer\, in identifying TFs for cell reprogramming. Specifically\, I will present the results of an in silico perturbation experiment for reprogramming fibroblast cells to hematopoietic stem cells and compare the outcomes to experimental results found in the literature. I will conclude the talk with a discussion of the drawbacks and limitations of the Geneformer model and provide an assessment of what will be needed in the future for digital biology to fully reap the benefits of large-scale foundation models.
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-7/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:Engineering,FSML,Science
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241022T132000
DTEND;TZID=America/Detroit:20241022T170000
DTSTAMP:20241018T223652Z
CREATED:20241018T223652Z
LAST-MODIFIED:20241018T223652Z
UID:10000786-1729603200-1729616400@micde.umich.edu
SUMMARY:Conference / Symposium:MICDE ACES Mini-Symposium 2024
DESCRIPTION:This year’s focus of the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium is connecting advanced algorithms\, artificial intelligence (AI)\, and high-performance computing (HPC) architectures to advance scientific discovery. The event showcases the work of the University of Michigan faculty members at the intersection of AI\, HPC\, and advanced algorithms. It also includes a panel discussion on the current trends in AI\, novel algorithms\, and computer architectures for efficient scientific applications.\nACES is an event that promotes state-of-the-art research at the University of Michigan and the current trends and hot topics in computational science and engineering. Furthermore\, it is the nucleus for increasing collaboration at the University of Michigan by offering an opportunity for faculty members to share their ideas and network during a reception. Take advantage of this exciting opportunity to connect\, learn\, and be inspired!
URL:https://micde.umich.edu/event/conference-symposiummicde-aces-mini-symposium-2024/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor
CATEGORIES:Aces,Ai In Science And Engineering,Artificial Intelligence,Computation,Computational Science,Engineering,Free,High Performance Computing,In Person,Interdisciplinary,Micde,Michigan Engineering,parallel computing,Research,Science,Scientific Computing,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241018T120000
DTEND;TZID=America/Detroit:20241018T130000
DTSTAMP:20241104T135643Z
CREATED:20241012T182153Z
LAST-MODIFIED:20241104T135643Z
UID:10000783-1729252800-1729256400@micde.umich.edu
SUMMARY:FSML Lecture Series: Domain decomposition and coupling data-driven models of fluid flows by Christopher Wentland\, Sandia National Labs
DESCRIPTION:Zoom link \n \nAbstract: Simulating complex physical systems often requires joining non-uniform subsystems\, which may be characterized by different geometries or mesh topologies. Coupling these separate subsystems often relies on time-intensive meshing workflows or empirical coupling models\, which may not generalize well across all operational regimes. The Schwarz alternating method proposes to overcome these issues\, establishing an effective domain decomposition framework that allows for the coupling of arbitrary geometries. This talk presents a brief history of Schwarz-based coupling work at Sandia National Laboratories\, along with recent work on combining the Schwarz alternating method with data-driven modeling approaches\, namely projection-based reduced order models (PROMs) and operator inference. This approach can generate surrogates that are capable of simulating advection-dominated fluid flows with higher accuracy and lower cost than comparable monolithic models\, aiding analysis in many-query applications such as uncertainty quantification and engineering design. Several nuances of the Schwarz algorithm and their impacts on model performance are explored\, specifically non-overlapping decompositions and PROM hyper-reduction under domain decomposition. A look into ongoing Schwarz coupling work at Sandia discusses existing challenges and efforts to apply this approach to relevant engineering problems.
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-6/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241007T140000
DTEND;TZID=America/Detroit:20241007T150000
DTSTAMP:20240920T130536Z
CREATED:20240920T130536Z
LAST-MODIFIED:20240920T130536Z
UID:10000751-1728309600-1728313200@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/micde-midas-graduate-information-session-2/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif
GEO:42.2914823;-83.7138452
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241001T150000
DTEND;TZID=America/Detroit:20241001T160000
DTSTAMP:20241011T124325Z
CREATED:20240925T142215Z
LAST-MODIFIED:20241011T124325Z
UID:10000772-1727794800-1727798400@micde.umich.edu
SUMMARY:MICDE/ME Seminar: Krishnan Mahesh\, Professor\, University of Michigan NAME
DESCRIPTION:Bio:  Krishnan Mahesh is a Richard B. Couch Professor of Naval Architecture and Marine Engineering at the University of Michigan. His research focuses on the simulation of complex\, multi-physics turbulent flows. Mahesh received his Bachelor’s degree in Mechanical Engineering from the Indian Institute of Technology (Mumbai)\, and in 1996 obtained his Ph.D. degree in Mechanical Engineering from Stanford University. He is a 2018 Fulbright-Nehru Specialist\, a Fellow of the American Physical Society\, an Associate Fellow of the American Institute of Aeronautics and Astronautics\, and a Fellow of the Minnesota Supercomputing Institute. Mahesh is a recipient of the CAREER Award from the National Science Foundation and the Francois N. Frenkiel Award from the American Physical Society. He has received the Taylor Award for Distinguished Research\, McKnight Presidential Fellowship\, Guillermo E. Borja Award\, and McKnight Land-Grant Professorship from the University of Minnesota. \nLarge Eddy Simulation of Turbulent Cavitating Flows\nCavitation is a complex multi-scale phenomenon that has implications from intense sound production to erosion in engineering applications. This talk will discuss our efforts at developing the large-eddy simulation capability for the simulation of turbulent cavitating flows. LES of cavitation is challenged by phase change modeling\, acoustic stiffness\, sharp multiphase fronts\, strong compressibility effects\, consistent accounting of nuclei\, broadband turbulence and subgrid effects. \nLES of partial cavitation will be discussed under the same conditions as experiments in a sharp wedge configuration.  Physical mechanisms of cavity transition observed in the experiments\, i.e.\, re-entrant jet and bubbly shock waves\, are both captured in the LES over their respective regimes. Vapor volume fraction data obtained from the LES will be quantitatively compared to X-ray densitometry\, and the results will be discussed. Cavitation nuclei are likely to be introduced through the free-stream as well as at solid surfaces. We will present a novel approach based on Gibbs free energy minimization to predict nuclei concentrations. The results from the proposed work will be applied to account for dissolved gas content in CSM measurements and predict several decades of experimentally observed trends in nuclei concentrations. Cavitating flows possess a range of vapor length scales ranging from tiny vapor bubbles to large vapor pockets. We will discuss a compressible hybrid model to capture both sub-grid vapor nuclei and massive sheet cavity dynamics. Finally\, physical aspects of inception due to the interaction of a counter–rotating vortex pair generated behind a pair of hydrofoils will be presented. \n\n  \nThe MICDE Fall 2025 Seminar Series is open to all. University of Michigan faculty and students. \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-me-seminar-krishnan-mahesh-professor-university-of-michigan-name/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
CATEGORIES:Computational Science,Engineering,Featured Events,Free,Mechanical Engineering,Micde Seminar,MICDE Seminar Series,Michigan Engineering,Naval Architecture and Marine Engineering
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/05/Mahesh-Krishnan-NAME.png
GEO:42.2929214;-83.7154247
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=2150 H.H. Dow 2300 Hayward St Ann Arbor 48109 United States;X-APPLE-RADIUS=500;X-TITLE=2300 Hayward St:geo:-83.7154247,42.2929214
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240930T150000
DTEND;TZID=America/Detroit:20240930T160000
DTSTAMP:20240920T130342Z
CREATED:20240920T130342Z
LAST-MODIFIED:20240920T130342Z
UID:10000750-1727708400-1727712000@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/micde-midas-graduate-information-session/
LOCATION:1100 North University Building – 1528
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240708T090000
DTEND;TZID=America/Detroit:20240726T160000
DTSTAMP:20240611T125750Z
CREATED:20240611T033416Z
LAST-MODIFIED:20240611T125750Z
UID:10000684-1720429200-1722009600@micde.umich.edu
SUMMARY:SciFM24 Summer School
DESCRIPTION:The SciFM Summer School runs from July 8 to July 26. The first instance is going to be run in parallel at the University of Michigan (UM) and at Argonne National Laboratory (ANL). The focus will be on developing technical plans for specific instances for SciFMs\, a hackathon to jumpstart the construction of SciFMs\, as an incubator for ideas\, a training ground for researchers and students\, and further a vision for a national SciFM ecosystem.
URL:https://micde.umich.edu/event/workshop-seminarscifm24-summer-school/
CATEGORIES:Aerospace Engineering,College Of Engineering,Computational Science,Engineering,Micde,Scientific Computing,Sciml,Workshops
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240419T110000
DTEND;TZID=America/Detroit:20240419T120000
DTSTAMP:20240412T140752Z
CREATED:20240412T011911Z
LAST-MODIFIED:20240412T140752Z
UID:10000680-1713524400-1713528000@micde.umich.edu
SUMMARY:MICDE Fellowships Information Session
DESCRIPTION:Applications for the $4\,500 2024-2025 MICDE Graduate Fellowships will open May 1\, 2024. Please join this Zoom session to learn more about the fellowships and how to submit a great application! \nThese fellowships are sponsored by the Michigan Institute for Computational Discovery & Engineering. For more information\, see https://live-umor-micde.pantheonsite.io/academic-programs/fellowships/.
URL:https://micde.umich.edu/event/livestream-virtualmicde-fellowships-information-session/
LOCATION:Zoom Event
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Astronomy,big data,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Modeling,Computational Science,Computational Social Science,computer science,Data Science,Earth And Environmental Sciences,Ecology,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Fellowship,Free,Funding,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Life Science,Lsaresearch,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Natural Language Processing,Natural Sciences,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,parallel computing,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scholarship,Science,Scientific Computing,Sciml,Sociology,Statistics,Virtual,Webcast,Workshops
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231205T120000
DTEND;TZID=America/Detroit:20231205T130000
DTSTAMP:20231204T192114Z
CREATED:20230926T191303Z
LAST-MODIFIED:20231204T192114Z
UID:10000654-1701777600-1701781200@micde.umich.edu
SUMMARY:MICDE Ph.D. Student Seminars 2023-2024: Jeffrey Hatch
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nComputational Methods in Chemistry\nAbstract coming soon… \nJeffrey Hatch\, Ph.D. candidate in Chemistry and Scientific Computing \nBio coming soon… \nRegister to attend this seminar
URL:https://micde.umich.edu/event/phd-seminar-jeffrey-hatch/
LOCATION:2022 South Thayer Building
CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Template-Speaker-3.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231128T120000
DTEND;TZID=America/Detroit:20231128T130000
DTSTAMP:20260522T152930Z
CREATED:20230914T150100Z
LAST-MODIFIED:20260522T152930Z
UID:10000641-1701172800-1701176400@micde.umich.edu
SUMMARY:MICDE Ph.D. Student Seminars 2023-2024: Guoer Liu
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nRegister to attend this seminar \nWhen Is Big Data Biased?\nUsing automation technology to gather and disseminate information to the public is commonly viewed as a government-led effort to enhance oversight and address the principal-agent problem in bureaucracy. However\, focusing on the expansion of China’s automatic ambient air quality monitoring network in the last decade (2012-2022)\, I argue that technology is being utilized as a tool to emphasize optics but overlook the substantive problems. I illustrate the idea with multiple original georeferenced data sets on the automatic monitoring network\, pollution sources\, and satellite-derived vegetation density across time and space. I show that\, while the automation initiative has improved the data quality in some ways\, the undersupply of automatic monitoring stations\, over-represented clean locations\, and non-random missing pollution records continue to contribute to inaccurate air pollution information. As long as political incentives to manipulate information persist\, actors can mold technology that operates without human intervention to serve their own interests. \nGuoer Liu\, Ph.D. candidate in Political Science and Scientific Computing\nGuoer Liu is a Ph.D. candidate in Political Science. Her dissertation project\, ‘‘From Oversight to Overlook’’ investigates how political determinants distort the technology infrastructure and create seemingly credible but inaccurate information to the public. \nAdvisors: Mary Gallagher\, Charles Shipan \nRegister to attend this seminar
URL:https://micde.umich.edu/event/phd-seminar-guoer-liu/
LOCATION:2022 South Thayer Building
CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231114T123000
DTEND;TZID=America/Detroit:20231114T130000
DTSTAMP:20231103T200940Z
CREATED:20230914T150100Z
LAST-MODIFIED:20231103T200940Z
UID:10000640-1699965000-1699966800@micde.umich.edu
SUMMARY:MICDE Ph.D. Student Seminars: Jamie Holber
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nActive Learning for Physics Informed Data Sampling and Construction of Free Energy Representations\nUsing automation technology to gather and disseminate information to the public is commonly viewed as a government-led effort to enhance oversight and address the principal-agent problem in bureaucracy. However\, focusing on the expansion of China’s automatic ambient air quality monitoring network in the last decade (2012-2022)\, I argue that technology is being utilized as a tool to emphasize optics but overlook the substantive problems. I illustrate the idea with multiple original georeferenced data sets on the automatic monitoring network\, pollution sources\, and satellite-derived vegetation density across time and space. I show that\, while the automation initiative has improved the data quality in some ways\, the undersupply of automatic monitoring stations\, over-represented clean locations\, and non-random missing pollution records continue to contribute to inaccurate air pollution information. As long as political incentives to manipulate information persist\, actors can mold technology that operates without human intervention to serve their own interests. \nJamie Holber\, Ph.D. candidate in Applied Physics and Scientific Computing \nJamie Holber is a PhD Candidate in Applied Physics and Scientific Computing working in the Computational Physics Group in the Mechanical Engineering Department. \nAdvisor: Krishna Garikipati \nRegister to attend this seminar
URL:https://micde.umich.edu/event/phd-seminar-jamie-holber/
LOCATION:2022 South Thayer Building
CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231114T080000
DTEND;TZID=America/Detroit:20231114T190000
DTSTAMP:20231020T163041Z
CREATED:20231020T163041Z
LAST-MODIFIED:20231020T163041Z
UID:10000660-1699948800-1699988400@micde.umich.edu
SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023
DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase
URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023-2/
LOCATION:Rackham Graduate School (Horace H.)
CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231113T080000
DTEND;TZID=America/Detroit:20231113T190000
DTSTAMP:20231020T163040Z
CREATED:20231020T163040Z
LAST-MODIFIED:20231020T163040Z
UID:10000659-1699862400-1699902000@micde.umich.edu
SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023
DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase
URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023/
LOCATION:Rackham Graduate School (Horace H.)
CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231107T120000
DTEND;TZID=America/Detroit:20231107T130000
DTSTAMP:20231019T200916Z
CREATED:20230914T150100Z
LAST-MODIFIED:20231019T200916Z
UID:10000639-1699358400-1699362000@micde.umich.edu
SUMMARY:MICDE Ph.D. Student Seminars: Bernardo Pacini & Srinivasan Arunachalam
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nRegister to attend this seminar \nGradient-Based Multidisciplinary Design Optimization for Propeller Design\nUrban air mobility (UAM) vehicles have taken form as advanced rotorcraft with sets of wings\, rotors\, canards\, and other appendages. Noise generation is an important technical barrier that must be addressed to prevent these vehicles from causing excessive disturbance to the communities they are intended to service. To understand the noise these vehicles generate\, and to develop designs that can minimize disturbance\, there is a need for analysis and optimization tools specifically for the conceptual design and sizing phase of urban air mobility vehicle development. Such tools must be computationally efficient to allow for the repeated analyses needed for design optimization. This presentation will review the work being carried out at the University of Michigan\, coupling aerodynamic\, structural\, and aeroacoustic disciplines within the multidisciplinary gradient-based design optimization framework OpenMDAO. While aerostructural optimization has been performed previously\, coupling with aeroacoustics is challenging given the requirement for time accurate simulations and the associated computational cost of such analyses. By leveraging multiple model fidelities and utilizing efficient gradient calculation techniques\, such as the adjoint method and algorithmic differentiation\, these disciplines can be formulated into an optimization framework that can be applied to UAM vehicle designs. This presentation will review the work completed to date\, including preliminary results\, and expand on the future goals of the project\, working towards a broader optimization framework for rotorcraft vehicle design optimization. \nBernardo Pacini\, Ph.D. candidate in Mechanical Engineering and Scientific Computing \nBernardo Pacini is a Ph.D. Candidate at the University of Michigan focusing his research on aerodynamic\, structural\, and aeroacoustic modeling for multidisciplinary design optimization of urban air mobility vehicles. He is a member of the Multidisciplinary Design Optimization Laboratory led by Professor Joaquim R. R. A. Martins and of the Computational Aerosciences Laboratory led by Professor Karthik Duraisamy. Bernardo’s work to date is on developing an aero-structural-acoustic analysis framework that can be implemented within the multidisciplinary design optimization process for rotorcraft and urban air mobility vehicle design. \nRegister to attend this seminar \nValidation of a multivariate non-Gaussian\, non-stationary wind pressure simulation model for performance-based wind engineering\nWith a growing interest in probabilistic performance assessments of building systems subjected to wind loads\, there is a demand for accurately representing building-specific wind loads\, considering their non-Gaussian and non-stationary features. While typical wind tunnel data collected for a set of discrete wind directions provide a single realization of stationary pressures\, there is currently no experimentally validated model for the stochastic simulation of non-Gaussian and non-stationary wind pressures that can be calibrated to wind tunnel datasets. Such a model is essential for simulating building aerodynamics\, especially the stochastic\, path-dependent responses associated with time-varying wind speed and direction experienced by a building during hurricane wind events. This talk will review a recently developed theoretical formulation for generating these stochastic pressures. Through carefully designed tests conducted at the University of Florida wind tunnel facility\, the formulation was extensively validated with respect to its ability to capture trends over time\, occurrences of peaks\, and time-varying frequency content. \nSrinivasan Arunachalam\, Ph.D. candidate in Civil and Environmental Engineering and Scientific Computing \nSrinivasan Arunachalam is a PhD candidate in Civil and Environmental Engineering. His research interests lie in uncertainty quantification and understanding the inelastic behavior of wind-excited structures. He is excited about the algorithmic developments that enable efficient reliability assessments\, as well as the evolving insights into the physics of extreme responses and their implications for structural design. \nRegister to attend this seminar
URL:https://micde.umich.edu/event/phd-seminar-bernardo-pacini-srinivasan-arunachalam/
LOCATION:2022 South Thayer Building
CATEGORIES:Computation,Computational Modeling,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Sessions,Talk
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230929T120000
DTEND;TZID=America/Detroit:20230929T130000
DTSTAMP:20230912T223037Z
CREATED:20230817T202936Z
LAST-MODIFIED:20230912T223037Z
UID:10000610-1695988800-1695992400@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/presentationgraduate-studies-in-computational-data-sciences-information-session-2-2/
LOCATION:1100 North University Building – 1528
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230927T160000
DTEND;TZID=America/Detroit:20230927T170000
DTSTAMP:20230912T222916Z
CREATED:20230817T202936Z
LAST-MODIFIED:20230912T222916Z
UID:10000612-1695830400-1695834000@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/presentationgraduate-studies-in-computational-data-sciences-information-session/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif
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DTSTART;TZID=America/Detroit:20230926T123000
DTEND;TZID=America/Detroit:20230926T130000
DTSTAMP:20230921T195259Z
CREATED:20230913T145952Z
LAST-MODIFIED:20230921T195259Z
UID:10000631-1695731400-1695733200@micde.umich.edu
SUMMARY:MICDE Ph.D. Student Seminar: Haowei Sun
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we ask that you register to attend the seminar. If you have any questions\, please email micde-events@umich.edu. \nRegister to attend this seminar \nTera City: Accurate and Efficient AV Safety Performance Evaluation\nThis talk is aiming at mitigating current simulation-based AV safety performance evaluation approaches. Tera City is composed of naturalistic driving environment construction and intelligent testing environment construction\, which provided accuracy and efficiency\, respectively. \nHaowei Sun\, Ph.D. candidate in Civil and Environmental Engineering and Scientific Computing \nHaowei Sun is a Ph.D. candidate at Civil Engineering\, Next Generation Transportation System Program. His research focus is mainly on the safety validation and verification of autonomous vehicles. \nRegister to attend this seminar
URL:https://micde.umich.edu/event/phd-seminar-haowei-sun/
LOCATION:2022 South Thayer Building
CATEGORIES:Civil and Environmental Engineering,Computation,Computational Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Michigan Engineering,Phd Seminar,Prospective Graduate Students,Rackham,Science,Scientific Computing,Seminar,Talk
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Haowei-Sun.png
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