Symposium: Advances on Turbulence Modeling
2017-07-13 09:00 - 17:30, 2017-07-12 09:00 - 17:30, 2017-07-11 09:00 - 17:30 (America/Detroit)
The Center for Data-Driven Computational Physics and NASA are sponsoring the event to discuss the state of the art in turbulence modeling from an academic and an industrial perspective, and place some of the newer developments in RANS modeling (such as uncertainty quantification, data-driven modeling, etc.) in the context of mainstream turbulence modeling.
Speakers include:

Florian Menter, Ansys
Suad Jakirlic, TU Darmstadt
Robert Moser, U. Texas

For more details and to register go to http://turbgate.engin.umich.edu/symposium/

2017 MICDE Annual Symposium
2017-04-18 08:30 - 17:00 (America/Detroit) | Rackham Building, 4th Floor, 915 E. Washington, Ann Arbor, MI, 48109, United States
Please join us for the Michigan Institute for Computational Discovery and Engineering 2017 Symposium. The event features eminent scientists from around the world and the U-M campus. The symposium this year focuses on the “New Era of Data-Enabled Computational Science.”
Speakers:

Frederica Darema — Director, Air Force Office of Scientific Research
George Karniadakis — Professor of Applied Mathematics, Brown University
Tinsley Oden — Director of the Institute for Computational Engineering and Sciences, V.P. for Research, University of Texas at Austin
Karen Willcox — Professor of Aerospace and Aeronautics, Massachusetts Institute of Technology, co-Director of MIT Center for Computational Engineering
Jacqueline H. Chen — Distinguished Member of Technical Staff at the Combustion Research Facility, Sandia National Laboratories
Laura Balzano — Assistant Professor, Electrical Engineering and Computer Science, U-M
Emanuel Gull — Assistant Professor, Physics

The symposium features a poster competition and more. For more information and to register go to https://live-umor-micde.pantheonsite.io/symposium17/
Past Symposia
2016 MICDE Annual Symposium
Research Computing Symposium Fall 2014

MICDE Seminar: Yongjie Jessica Zhang, Mechanical Engineering and Biomedical Engineering, Carnegie Mellon University
2017-03-17 11:00 - 12:00 (America/Detroit) | 1200 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Yongjie Jessica Zhang is a Professor in Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering, and M.Eng. in Engineering Mechanics from Tsinghua University, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin. After staying two years at ICES as a postdoctoral fellow, she joined CMU in 2007 as an assistant professor, and then was promoted to an associate professor in 2012 and a full professor in 2016. Her research interests include computational geometry, mesh generation, computer graphics, visualization, finite element method, isogeometric analysis and their application in computational biomedicine, material sciences and engineering. She has co-authored over 140 publications in peer-reviewed journals and conference proceedings, and received the Autodesk Best Paper Award 1st Place in SIAM Conference on Solid and Physical Modeling 2015, the Best Paper Award in CompIMAGE’16 conference and one of the 5 Most Highly Cited Papers Published in Computer-Aided Design during 2014-2016. She recently published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press, Taylor & Francis Group. She is the recipient of Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, USACM Gallagher Young Investigator Award, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, George Tallman Ladd Research Award, and Donald L. & Rhonda Struminger Faculty Fellow.
Image-Based Mesh Generation and Volumetric T-Spline Modeling for Isogeometric Analysis with Engineering Applications
With finite element method and scanning technology seeing increased use in many research areas, there is an emerging need for high-fidelity geometric modeling and mesh generation of spatially realistic domains. This talk will highlight research in three areas: image-based mesh generation for complicated domains, trivariate spline modeling for isogeometric analysis, as well as biomedical, material sciences and engineering applications. First Prof. Zhang will present advances and challenges in image-based geometric modeling and meshing along with a comprehensive computational framework, which integrates image processing, geometric modeling, mesh generation and quality improvement with multi-scale analysis at molecular, cellular, tissue and organ scales. Different from other existing methods, the presented framework supports five unique features: high-fidelity meshing for heterogeneous domains with topology ambiguity resolved; multiscale geometric modeling for biomolecular complexes; automatic all-hexahedral mesh generation with sharp feature preservation; robust quality improvement for non-manifold meshes; and guaranteed-quality meshing. These unique capabilities enable accurate, stable, and efficient mechanics calculation for many biomedicine, materials science and engineering applications. As a new advancement of traditional finite element method, isogeometric analysis (IGA) was proposed to integrate design and analysis. In the second part of this talk, she will present her latest research on volumetric T-spline parameterization for IGA applications. For arbitrary-topology objects, we first build a polycube whose topology is equivalent to the input geometry and it serves as the parametric domain for the following trivariate T-spline construction. Boolean operations, geometry skeleton and centroidal Voronoi tessellation based surface segmentation are used to preserve surface features. A parametric mapping is then used to build a one-to-one correspondence between the input geometry and the polycube boundary. After that, we choose the deformed octree subdivision of the polycube as the initial T-mesh, and make it valid through pillowing, quality improvement, and applying templates or truncated subdivision schemes to handle extraordinary nodes. Weighted and truncated T-spline basis functions are derived to enable analysis-suitability, including partition of unity and linear independence. The developed pipelines have been incorporated into commercial software such as Rhino and Abaqus.
Prof. Zhang is being hosted by Prof. Garikipati (Mechanical Engineering)

SC2/MICDE Seminar: Eric Jankowski, Materials Science and Engineering, Boise State University
2017-03-08 14:00 - 15:00 (America/Detroit) | 2540 G.G. Brown (2350 Hayward St.), 2300 Hayward St, Ann Arbor, MI, 48109, United States
Bio: Eric Jankowski is an assistant professor of Materials Science and Engineering at Boise State University. He earned his PhD in Chemical Engineering from the University of Michigan in 2012, where he developed computational tools to study the self-assembly of nanoparticles. These tools leveraged graphics processors to accelerate computations and provided insight into systems of both theoretical and practical importance. Dr. Jankowski began focusing on renewable energy generation during his postdoctoral positions at the University of Colorado and the National Renewable Energy Laboratory. At these postdocs, Dr. Jankowski applied techniques he developed during his thesis to understand factors that determine the ordering of molecules in organic solar cells.
This is a joint seminar of the Scientific Computing Student Club and MICDE, sponsored in part by U-M Rackham Graduate School.

Cobbling together computational components to engineer inexpensive plastic solar panels
In order to meet projected global energy demands over the next 25 years, the equivalent of building a 1GW power plant each day is needed. Existing clean power generation technologies can meet this demand in principle, but their relatively large short-term costs have limited widespread adoption. In this work we explain manufacturing strategies for organic (plastic) solar panels that overcome economic barriers to adoption by optimizing the structure of the organic active layer responsible for generating electricity. We perform coarse-grained molecular dynamics simulations accelerated with graphics processing units to determine the thermodynamically stable morphologies for a variety of candidate ingredients. Using these morphologies we perform kinetic Monte Carlo charge transport simulations to determine which morphologies are better candidates for solar devices. The simulation pipeline developed here combines computational tools developed for solving unrelated problems, and we discuss the evolving landscape of scientific computing education and how it overlaps with this work.

MICDE Seminar: Michael Eldred, Computation, Computers, Information, and Mathematics Center, Sandia National Laboratories
2017-03-07 16:00 - 17:00 (America/Detroit) | 1008 FXB, 1320 Beal Ave, Ann Arbor, MI, 48109
Bio: Michael Eldred is a Distinguished Member of the Technical Staff in the Optimization and Uncertainty Quantification Department within the Computation, Computers, Information, and Mathematics Center at Sandia National Laboratories. He received his B.S. in Aerospace Engineering from Virginia Tech in 1989, his M.S.E. and Ph.D. in Aerospace Engineering from the University of Michigan in 1990 and 1993. Mike led the DAKOTA project, a “… toolkit that provides a flexible, extensible interface between analysis codes and iterative systems analysis methods…”, for 15 years (1994-2009) and now leads algorithm research and development activities related to DAKOTA. Mike’s research interests include uncertainty quantification, design under uncertainty, surrogate-based optimization, and high-performance computing, with application to stockpile stewardship and energy initiatives through the NNSA ASC, DOE ASCR, and DOE SciDAC programs.
Mike is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and a member of the Society for Industrial and Applied Mathematics (SIAM), the International Society for Structural and Multidisciplinary Optimization (ISSMO), and the United States Association for Computational Mechanics (USACM). He currently serves as a member of the AIAA Nondeterministic Approaches Technical Committee and on the editorial board for the International Journal for Uncertainty Quantification. A number of his publications are available on the DAKOTA web site.
Title: Multilevel-Multifidelity Approaches for Uncertainty Quantification and Design
In the simulation of complex physics, multiple model forms of varying fidelity and resolution are commonly available. In computational fluid dynamics, for example, common model fidelities include potential flow, inviscid Euler, Reynolds-averaged Navier-Stokes, and large eddy simulation, which may be further augmented by subgrid-scale model selections and spatio-temporal discretization levels. In this presentation, we focus on novel algorithms that simultaneously exploit multiple model forms and multiple resolutions, both for uncertainty quantification (UQ) and for optimization under uncertainty (OUU). These hybrid methods exploit multifidelity methods across the model form hierarchy in combination with multilevel accelerators across an associated discretization hierarchy, manifesting as multilevel control variate Monte Carlo and multilevel polynomial expansion methods in the UQ case and recursive trust-region and multigrid optimization in the OUU case. These techniques will be demonstrated for both model problems and engineered systems, and will be placed within the broader context of algorithm research and development within the Dakota project at Sandia.
Dr. Eldred is being hosted by Prof. Duraisamy (Aerospace Engineering)

[SC2] U-M 3D Lab Tour
2017-02-16 17:00 - 18:00 (America/Detroit)
The 3D Lab will give a brief tour and presentation of the 3D visualization resources available to students and all researchers at U-M. If you are thinking of registering to the Visualization Challenge, this is a good opportunity to see many of the 3D options you have to present your data. Space is limited to 20 people: register here

MICDE Seminar: Steven White, Physics & Astronomy, University of California Irvine
2017-02-14 15:00 - 16:00 (America/Detroit) | 340 West Hall, 1085 South University Ave., Ann Arbor, MI, 48109, United States
Bio: Steven White did his bachelor’s degree at the University of California in San Diego and received his Ph.D. from Cornell University. Early in his career he was awarded a National Science Foundation fellowship, and an IBM postdoctoral fellowship. He’s been named an American Physical Society fellow, and a fellow of the American Association for the Advancement of Science, and of the American Academy of Arts and Science, among others. Professor White is most known for inventing the Density Matrix Renormalization Group (DMRG), a numerical variation technique for high accuracy calculations of the low energy physics of quantum many-body systems. In 2003 he won the American Physical Society Aneesur Rahman prize, a recognition of outstanding achievement in computational physics research “…for his development, application, and dissemination of the DMRG method”. He has published over one hundred and seventy papers on this and related subjects.
Tensor Network methods for Electronic Structure
Our conventional picture of wave functions living in an exponentially large Hilbert space is both impractical for solving many particle systems and conceptually lacking: in recent years we have come to understand that physical states of matter live in an infinitesimal corner of Hilbert space, characterized primarily by low entanglement. Tensor networks are the natural language to express low entanglement wave functions, giving an exponentially compressed description of ground states. The density matrix renormalization group (DMRG) and other tensor network algorithms have had tremendous success in simulating quantum lattice models.The key challenge in translating these methods to electronic structure is the need to represent continuum space in an efficient way. After an introduction to tensor networks, I’ll present a new DMRG-based approach suitable for the electronic structure of long molecules. Our sliced-basis DMRG method produces near-exact ground states within its basis, and has a computation time which is linear in the length of the molecule. We are implementing SBDMRG for chains of hydrogen atoms, where we have been able to simulate up to 1000 atoms in a minimal basis.
Prof. White is being hosted by Prof. Emanuel Gull (Chemistry)

MICDE Seminar: Anna Krylov, Chemistry, University of Southern California
2017-02-03 10:00 - 11:00 (America/Detroit) | CHEM 1640, 930 N University, Ann Arbor, MI, 48109, United States
Bio: Anna Krylov is a Gabilan Distinguished Professor in Science and Engineering, Chemistry at the University of Southern California. She received her M.Sc. in Chemistry from Moscow State University and later her Ph.D. from The Hebrew University of Jerusalem. Upon completing her Ph.D. in 1996 (summa cum laude), she joined the group of Prof. Martin Head-Gordon at the University of California, Berkeley as a postdoctoral research associate, where she first became involved with electronic structure method development. In 1998, she joined Department of Chemistry at USC. Currently, Prof. Krylov leads a research group focused on theoretical modeling of open shell and electronically excited species. She is the head of the Center for Computational Studies of Electronic Structure and Spectroscopy of Open-Shell and Electronically Excited Species, iOpenShell, supported by the National Science Foundation (2005–2011) and the University of Southern California. She is developing robust black-box methods aiming to describe complicated multi-configurational wave functions in a single-reference formalism, such as coupled-cluster and equation-of-motion (or linear response) approaches. She has developed the spin-flip approach, which extends coupled-cluster and density functional methods to diradicals, triradicals, and bond-breaking. Using computational chemistry tools, and in collaboration with numerous experimental groups, Krylov is also investigating the role that radicals and electronically excited species play in such diverse areas as combustion, gas- and condensed-phase chemistry, solar energy applications, bioimaging, and ionization-induced processes in biology. She has co-authored more than 120 publications and has delivered more than 130 invited lectures. (Source https://en.wikipedia.org/wiki/Anna_Krylov)
Fission of entangled spins: Electronic structure perspective
Singlet fission (SF), a process in which one singlet excited state is converted into two triplet states, is of interest in the context of organic photovoltaic technology. Owing to its technological significance, the mechanism of SF has been vigorously investigated. Yet, the design principles for materials capable of efficient SF remain elusive. The main challenge faced by theory is a complex and intricate electronic structure of the process, which involves non-adiabatic transitions between strongly correlated states. This lecture will discuss electronic structure of the relevant states, the nature of non-adiabatic couplings, and the connection between electronic factors and rates, emphasizing the methodological aspects of the problem. The utility of theory will be illustrated by examples. Recent experimental and theoretical studies of SF in covalently linked tetracene dimers shed light on the effect of the linkers on the electronic factors and SF rates, illuminating the role of through-space and through-bond interactions between the chromophores. The results highlight the importance of integrative approaches that evaluate the overall rate, rather than focus on specific electronic factors, such as energies or couplings.

MICDE Seminar: Vipin Kumar, Computer Science and Engineering, University of Minnesota
2017-01-27 12:00 - 13:00 (America/Detroit) | 1008 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering at the University of Minnesota. His research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data driven approaches. He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book “Introduction to Parallel Computing”, and “Introduction to Data Mining”. Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association). Kumar is a Fellow of the ACM, IEEE and AAAS. He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013) and the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009). Kumar’s foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society’s highest awards.
Big Data in Climate: Opportunities and Challenges for Machine Learning and Data Mining
This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data mining and machine learning approaches for analyzing massive amount of climate and ecosystem data now available from satellite and ground-based sensors, and physics-based climate model simulations. These information-rich data sets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth’s ecosystem and for advancing the science of global change. This talk will discuss challenges in analyzing such data sets and some of our research results in mapping the dynamics of surface water globally as well as detecting deforestation and fires in tropical forests using data from Earth observing satellites.
Research funded by the NSF Expeditions in Computing Program and NASA
Pizza lunch will be provided

Graduate Studies in Computational & Data Sciences Info Session - North Campus
2017-01-11 17:00 - 18:00 UTC | Johnson Rooms, Lurie Engineering Center, 3rd Floor, 1221 Beal Ave., Ann Arbor, MI, United States
Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Graduate Studies in Computational & Data Sciences Info Session - Central Campus
2017-01-09 17:00 - 18:00 UTC | 2001 LSA Building, 500 State St., Ann Arbor, MI, 48109, United States

MICDE Seminar: Ann Almgren, Lawrence Berkeley National Lab
2016-12-09 11:00 - 12:00 UTC | 1013 H. H. Dow, 2300 Hayward St., Ann Arbor, MI, 48109, United States
Bio: Ann Almgren is a senior scientist in the Computational Research Division of Lawrence Berkeley National Laboratory and the Group Lead of the Center for Computational Sciences and Engineering. Her primary research interest is in computational algorithms for solving PDE’s for fluid dynamics in a variety of application areas. Her current projects include the development and implementation of new multiphysics algorithms in high-resolution adaptive mesh codes that are designed for the latest multicore architectures. She is a SIAM Fellow and serves on the editorial boards of CAMCoS and SIREV.
Next-Generation AMR
Block-structured adaptive mesh refinement (AMR) is a powerful tool for improving the computational efficiency and reducing the memory footprint of structured-grid numerical simulations. AMR techniques have been used for over 25 years to solve increasingly complex problems. I will give an overview of recent and planned advances in AMR algorithms and implementations at BerkeleyLab to address the challenges of next-generation multicore architectures and the complexity of multiscale, multiphysics problems. This will include new ways of thinking about multilevel algorithms and new approaches to data layout and load balancing, in situ and in transit visualization and analytics, and run-time performance modeling and control.

Computational Research at Lawrence Berkeley National Lab
2016-12-08 17:00 - 18:00 (America/Detroit)
MICDE invites students to a presentation by Dr. Ann Almgren from the Computational Research Division (CRD) at Lawrence Berkeley National Lab (LBL). Dr. Almgren will give an overview of CRD and what type of research goes on in applied mathematics, scientific computing / computational science, computer science and data science & technology. This presentation will focus on students who might be interested in postdoc positions at LBL at some point in their future.
Food and drinks will be provided!

MICDE/RadLab/IEEE Seminar: Levent Gürel, ABAKUS Computing Technologies
2016-12-02 14:00 - 15:00 (America/Detroit) | 3427 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Prof. Levent Gürel (Fellow of IEEE, ACES, and EMA) received the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991, respectively, in electrical and computer engineering. He worked at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York, in 1991-94. During his 20 years with Bilkent University, he served as the Founding Director of the Computational Electromagnetics Research Center (BiLCEM) and a professor of electrical engineering. He is also an Adjunct Professor at UIUC. Prof. Gürel is the Founder and CEO of ABAKUS Computing Technologies, a company that is geared towards advancing the use of cutting-edge computing technologies for solving difficult scientific problems with important real-life applications and societal benefits. He is conferred the UIUC ECE Distinguished Alumni Award in 2013 and the IEEE Harrington-Mittra Award in Computational Electromagnetics in 2015. He is an IEEE Distinguished Lecturer. He was invited to address the 2011 and 2017 ACES Conferences as a Plenary Speaker and a TEDx Conference in 2014. Among other recognitions of Prof. Gürel’s accomplishments, the two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technological Research Council of Turkey (TUBITAK) in 2003 are the most notable. Since 2003, Prof. Gürel has been serving as an associate editor for Radio Science, IEEE Transactions on Antennas and Propagation, IEEE Antennas and Wireless Propagation Letters, IET Microwaves, Antennas & Propagation, JEMWA, PIER, ACES Journal, and ACES Express.
Solution of Extremely Large Forward and Inverse Problems in Computational Electromagnetics: BIG DATA Aspects
As we solve some of the largest problems in the interdisciplinary domain of computational electromagnetics, we have to deal with various aspects of big-data issues routinely. Most recently, we have achieved the solutions of larger than 1,500,000,000×1,500,000,000 (1.5 billion!) dense matrix equations! This achievement is an outcome of a multidisciplinary effort involving physical understanding of electromagnetics problems, novel parallelization strategies (computer science), constructing parallel clusters (computer architecture), advanced mathematical methods for integral equations, fast solvers, iterative methods, preconditioners, linear algebra, and big data. Solving such large problems on a regular basis requires the generation, representation, storage, processing, analysis, transfer and communication, visualization and interpretation of extremely large data sets in the order of multiple terabytes.
Accurate formulations of real-life electromagnetics problems with integral equations necessitate the solution of extremely large dense matrix equations. Solutions of such tremendously challenging problems cannot be achieved easily, even when using the most powerful computers with state-of-the-art petascale computing capabilities. Instead, we have been solving some of the world’s largest integral-equation problems in computational electromagnetics by employing fast algorithms implemented on parallel computers. To achieve optimal management of multiple large data sets, we design and implement the handling of data in various levels of cache, memory, and disk, leading to meticulously designed out-of-core (OoC) schemes. That way, we enable the solution of unprecedentedly large problems with limited amounts of DRAM. In order to avoid decelerating the solution, we optimize communications among CPU cores, among processors, among nodes, from CPU to disk (and back), and in the case of heterogeneous architectures, we carefully control the data traffic to/from GPUs. Furthermore, we employ MPI and OpenMP simultaneously in a parallelization strategy designed to reduce data duplications among processes so that vast numbers of cores can be efficiently utilized without requiring extra memory.
I will present fast and accurate solutions of large-scale electromagnetic forward and inverse problems involving three-dimensional geometries that are larger than 1000 wavelengths using the multilevel fast multipole algorithm (MLFMA) and parallel MLFMA. Solving the world’s largest computational electromagnetics problems has important implications in terms of obtaining the solutions of future grand-challenge problems in imaging, (subsurface), optics, nanotechnology, bio-electromagnetics, metamaterials, remote sensing, as well as plethora of other disciplines of science, e.g., acoustics, elastics, quantum mechanics, astrophysics, molecular dynamics, electro-statics, fluid dynamics, thermodynamics. For more information: http://captains.of.computing.technology/.

MICDE 2016 Catalyst Grants Informational Session
2016-11-10 14:00 - 15:00 (America/Detroit) | Michigan League, Room D, 911 N. University , Ann Arbor, MI, 48109, United States
The Michigan Institute for Computational Discovery & Engineering (MICDE) seeks proposals for innovative research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure. Of interest is computational science research in any emerging area, including but not limited to (a) applications such as neuroscience, ecology, environmental science, evolutionary biology, human-made complex systems, urban infrastructure and energy; and (b) frameworks for scientific software, and exascale computing. Priority will be given to high-impact projects with potential to attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100,000 each.
In this informational session, MICDE officials will clarify the program’s intent, answer questions and facilitate team formation among attendees.
Please pre-register using this google form. You’ll need to be signed into your umich account. The session will be broadcasted via this bluejeans link. For more information go to https://live-umor-micde.pantheonsite.io/grants/catalyst-grants/

MICDE Seminar: Andrea Lodi, Polytechnique Montréal
2016-10-26 16:10 - 17:00 UTC | Boeing Auditorium – 1109 Francois-Xavier Bagnoud Building, 1320 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Andrea Lodi received a PhD in System Engineering from the University of Bologna in 2000 and he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY from 2005–2006. He was a full professor of Operations Research at DEI, University of Bologna between 2007 and 2015. Since 2015 he has been the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at the Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received recognition including the IBM and Google faculty awards. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization. He serves as Associate Editor for several prestigious journals in the area. He has been the network coordinator and principal investigator of two large EU projects/networks, and, since 2006, consultant of the IBM CPLEX research and development team. Finally, Andrea Lodi is the co-principal investigator (with Yoshua Bengio) of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution”, recently funded by the Canadian Federal Government under the Apogée Programme.
On Wide Split Cuts for Mixed-Integer Programming
Cutting planes (or simply cuts) are a fundamental component of modern Mixed-Integer Linear Programming (MILP) solvers because they help in strengthening the linear programming relaxation, a proxy to make the branchand-bound tree small. A classical way of devising cuts is to exploit disjunctions, for example in the domain of an integer variable, where, of course, no fractional value leads to any feasible solution. Cutting planes of this type, called split cuts, classically exploit disjunctions whose ‘width’ is always equal to one, i.e., no fractional value is feasible between two consecutive integer values. We investigate cutting planes that arise when widening the associated disjunctions. This allows, e.g., to model non contiguous domains of (integer) variables (or, stated differently, ‘holes’ in the domains). The validity of the disjunctions in a MILP can come from either primal or dual information, and we present examples and computational results in both cases. We further explore an exact MILP approach based on these cutting planes, that in addition tackles non-contiguity directly via branching and as a side-effect reduces the model size. (Joint work with P. Bonami, F. Serrano, A. Tramontani, S. Wiese.)
This seminar is co-sponsored by the U-M Department of Industrial & Operations Engineering

MICDE Seminar: Anthony Wachs, University of British Columbia
2016-10-14 15:10 - 16:00 UTC | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Anthony Wachs is an assistant professor with a joint appointment in the departments of Mathematics and of Chemical and Biological Engineering at the University of British Columbia, Vancouver, Canada. He received his B. Sc. and M. Sc. from the University Louis Pasteur of Strasbourg and his PhD from the Institut National Polytechnique of Grenoble in 2000. Right after, he was hired in 2001 as a Fluid Mechanics research engineer at IFP Energies nouvelles (IFPEN, at that time Institut Français du Pétrole) in Paris.
In 2009, he spent a one-year sabbatical at the nuclear research center of Cadarache in the south of France, where he worked for IRSN (the french national safety administration for nuclear energy). In 2010, he got his HDR (French Habilitation to Supervise Research) and was later promoted Scientific Advisor at IFPEN in Multiphase Flows and Scientific Computing. He then moved to IFPEN-Lyon where he supervised a group of researchers (including PhD and post-doc students) on the numerical simulation of reactive particulate flows (www.peligriff.com).
His main research interests are non-Newtonian Flows, Multiphase Flows and High Performance Computing. He collaborates extensively with academic groups in Canada, Brazil, France and Germany.
Micro/meso numerical modeling of flows laden with particles of arbitrary shape
Particulate flows are ubiquitous in environmental, geophysical and engineering processes. The intricate dynamics of these two-phase flows is governed by momentum transfer between the continuous fluid phase and the dispersed particulate phase. When significant temperature differences exist between the fluid and particles and/or chemical reactions take place at the fluid/particle interfaces, the phases also exchange heat and/or mass, respectively. While some multi-phase processes may be successfully modelled at the continuum scale through closure approximations, an increasing number of applications require resolution across scales, e.g. dense suspensions, fluidized beds. Within a multi-scale micro/meso/macro-framework, we develop robust numerical models at the micro and meso scales, based on a Distributed Lagrange Multiplier/Fictitious Domain method and a two-way Euler/Lagrange method, respectively. Collisions between finite size particles are modeled with a Discrete Element Method. Many real-life processes and/or flows involve non-spherical particles. Although there is still a lot to learn about flows laden with spherical particles, there is also a strong incentive to develop new modeling tools to account for non-spherical, angular, convex or even non-convex particles. We discuss assorted issues related to the numerical modelling of flows laden with particles of arbitrary shape. Along the way, we also address high performance computing issues related to our massively parallel numerical tools and challenges to efficiently transfer knowledge from small scales to large scales. We illustrate the modelling capabilities of our tools on the two following problems relevant of applications from the chemical engineering and process industry: (i) a rotating drum filled with non-convex particles and (ii) fixed and fluidized beds of multilobic (and hence non-convex) particles.

This seminar is co-organized with the Applied Interdisciplinary Mathematics program

MICDE Seminar: Jonathan Freund, University of Illinois at Urbana-Champaign
2016-10-06 15:45 - 17:00 UTC | Boeing Auditorium – 1109 Francois-Xavier Bagnoud Building, 1320 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Jonathan Freund is the Donald Biggar Willett Professor of Mechanical Science & Engineering and Aerospace at the University of Illinois at Urbana-Champaign. He is a Fellow of the American Physical Society, and a winner of the 2008 Frenkiel Prize from its Division of Fluid Dynamics where he currently serves as the division secretary/treasurer. He is an associate editor of Physical Review Fluids and on the editorial board of Annual Review of Fluid Mechanics. Computational science has been central to his research, which has included simulations of turbulent jet noise and its control, the dynamics of molecularly thin liquid films, nanostructure formation by ion-bombardment of semiconductor materials, and most recently the dynamics of red blood cells flowing in the narrow confines of the microcirculation. He co-directs the DOE-funded Center for Exascale Simulation of Plasma-Coupled Combustion at the University of Illinois.
Adjoint-based optimization for understanding and reducing flow noise
Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions. Increasing computer resources will be a key factor in improving these predictions still further. Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization. Predictions do not of themselves lead to quieter designs. They must be interpreted or harnessed in some way that leads to design improvements. As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity. In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs. This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized. The talk will review the formulation of the adjoint of the compressible flow equations for optimizing noise-reducing controls and present examples of its use. We will particularly focus on some mechanisms of flow noise that have been revealed via this approach.
This seminar is co-sponsored by U-M Aerospace Engineering

MICDE Seminar: Jeremy Lichstein, University of Florida
2016-09-29 16:00 - 17:00 UTC | 1210 Chemistry & Willard H Dow Laboratory, 930 University Ave., Ann Arbor, MI, 48109, United States
Bio: Jeremy Lichstein is an assistant professor of Biology at the University of Florida. Professor Lichstein got his Ph. D. from Princeton University and was a postdoctoral research fellow at Princeton’s department of Ecology and Evolutionary Biology. He was the recipient of the University of Florida Excellence Award for Assistant Professor, and was named a Florida Climate Institute Fellow for 2016-2017. His research interests are forest dynamics, biodiversity, carbon cycle and climate change.
Biodiversity and the changing Earth System: computational challenges and new answers to old questions
Terrestrial ecosystems currently offset roughly 25% of global annual anthropogenic fossil fuel emissions. However, the fate of this carbon sink is highly uncertain, in large part because global models diverge in their predictions of ecosystem responses to climate change, drought, and other perturbations. Although there is little agreement on how terrestrial ecosystems will respond to global change on decadal and longer time-scales, there is wide consensus that current global models are overly simplistic in their representation of important ecological processes. I will discuss our current understanding of how tree functional diversity is maintained in forests, the consequences of including more realistic levels of functional diversity in global models, and the computational challenges that need to be overcome in order to introduce ecological realism into the Earth System Models that the scientific and policy communities rely on for climate projections. A key result that is emerging from empirical and theoretical studies is that shifts in species composition across time or space (beta diversity) have different (and sometimes opposite) effects on ecosystem stability as local (alpha) diversity.
This seminar is co-sponsored by the U-M department of Ecology and Evolutionary Biology

MICDE Seminar: Rob Gardner, University of Chicago
2016-09-22 16:00 - 17:00 UTC | 340 West Hall, 1085 South University Ave., Ann Arbor, MI, 48109, United States
Bio: Robert Gardner is a Senior Scientist at the Computation Institute from the University of Chicago, and aSenior Scientist in the Enrico Fermi Institute. He spent his early academic career doing experimental high-energy physics research at different universities in the Midwest. He has been a member of the ATLAS experiment using the Large Hadron Collider at the CERN Laboratory, Geneva, Switzerland since 1998. His experimental work led him to specialize in developing and improving distributed computing technologies necessary for discoveries at the frontier of particle physics. He was instrumental in developing early research computing grids in the U.S.: the International Virtual Data Grid Laboratory (iVDGL), and the first deployment of the Open Science Grid (OSG) (NSF, Department of Energy). He have also generated systems for metrics collection for distributed systems (Grid Telemetry, PI, NSF-ITR). Currently, he directs the ATLAS Midwest Tier2 Center, which is comprised of integrated computing facilities from the University of Chicago, Indiana University, and the University of Illinois.
Leadership cyberinfrastructure for science and the humanities
In the past two decades high energy physics transformed its computing model from one relying on a single high performance computing center at the host laboratory to one incorporating resources distributed across institutional boundaries and geographic regions. Given the complexity of detectors and scale of data, the international collaborations of the Large Hadron Collider at CERN demanded it. By removing barriers to resource sharing, the resulting data and computation platform democratized the physics process across collaborations. Accelerated modes of scientific discovery by thousands of physicists were forged using hundreds of data centers linked by very high bandwidth networks. Meanwhile the explosion of commercial, social and enterprise data has driven innovation in resource abstraction and the creation of new service platforms, offering fresh opportunities to accelerate science and intellectual inquiry at all scales and across all domains. In this talk I’ll discuss the strategic significance that cyberinfrastructure technology plays in this regard and describe models for creating ubiquitous “substrates” that remove obstacles to connecting campuses, facilities, instruments and researchers.
This seminar is co-sponsored by the U-M department of Physics

Graduate Studies in Computational & Data Sciences Info Session - Central Campus
2016-09-21 17:00 - 18:00 UTC | 2001 LSA Building, 500 State St., Ann Arbor, MI, 48109, United States
Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan. This year we will offer a new practicum option through the Multidisciplinary Design Program.
The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
1) Modeling — Understanding of core data science principles, assumptions and applications;
2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Graduate Studies in Computational & Data Sciences Info Session - North Campus
2016-09-19 17:00 - 18:00 UTC | Johnson Rooms, Lurie Engineering Center, 3rd Floor, 1221 Beal Ave., Ann Arbor, MI, United States
