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Oct 07
0

Research Highlight: Improving aircraft aeropropulsive performance with multidisciplinary design optimization

By | News, Research

Anil Yildirim, Ph.D. Candidate, Aerospace Engineering

MICDE fellow Anil Yildirim, a Ph.D. candidate in the department of Aerospace Engineering, is working towards improving the overall efficiency of commercial tube-and-wing aircraft. The current commercial aircraft design with underwing engines have been the norm since the introduction of the Boeing 707 in the late 50’s [1]. With technological progress in composite materials and electric propulsion, as well as advancement of computational methods and computer power, researchers are developing more energy efficient systems to replace this legacy design. Working with the MDO Lab, lead by Prof. Joaquim R.R.A. Martins, and a team from NASA, Anil is studying the boundary layer ingestion (BLI) system on the STARC–ABL concept, introduced by NASA in 2016 [2] . BLI is an aeropropulsive concept, where a propulsion system is used to ingest the boundary layer generated by the aircraft. This increases propulsive efficiency and reduces the energy dissipated in the wake, effectively improving the overall aeropropulsive performance of the aircraft. Anil and his colleagues in the MDO Lab are using multidisciplinary analysis and optimization tools to study similar technologies, where design intuition is limited and interdisciplinary trades are important. Watch this video to learn more about his work (Authors: Anil Yildirim, Justin S. Gray, Charles A. Mader, Joaquim R. R. A. Martins, DOI: https://doi.org/10.2514/6.2019-3455)

 

[1] “707/720 Commercial Transport: Historical Snapshot,” 2015, http://www.boeing.com/history/
products/707.page
[2] https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160007674.pdf

May 28
0

Research Opportunity, Mechanical Engineering, TREE Lab – Summer 2019

By | Educational, Research, SC2, SC2 jobs

Dr. Bala Chandran’s Research Group, Mechanical Engineering, TREE Lab

Dr. Bala Chandran is seeking a highly motivated graduate (doctoral or masters) student interested in
doing research in the broad area of understanding radiative heat transfer in granular and
suspension flows via computational modeling for applications of high-temperature
energy storage and catalysis applications. Applicants are expected to have a sound
knowledge of fluid/continuum mechanics and the fundamentals of heat-transfer;
experience in complex fluids or multiphase flows is desirable, though not essential.
Applicants should be interested in the computational aspects of this project to develop
and write code.

Qualifications

  • Strong analytical and computational skills, and intellectual independence (i.e.,
    able to read books and papers and learn by oneself; able to apply theoretical
    knowledge to practical situations)
  • Relevant course work and experience related to
    • Undergraduate level fluid mechanics, solid mechanics, heat transfer,
      radiation, numerical methods and programming, computational fluid/solid
      mechanics
    • Graduate level courses on any/all of the above topics will be a plus point
  • Excellent professional and work ethic
  • Team player that is ready to interface with people developing experiments on
    this project

Application Procedure

If you are interested in this opportunity, please email Prof. Bala Chandran
(rbchan@umich.edu) all the following documents AS SOON AS POSSIBLE:

  1. A 2-page CV with references listed
  2. Unofficial academic transcript
  3. 1 one-page (maximum) statement of interest that explains why you are best suited for working on the proposed research topic and indicates how you meet the required project criteria.
  4. Slides (maximum 5) that showcase your research experience and contributions
Mar 07
0

Balzano wins NSF CAREER award for research on machine learning and big data involving physical, biological and social phenomena

By | General Interest, Happenings, News, Research

Prof. Laura Balzano received an NSF CAREER award to support research that aims to improve the use of machine learning in big data problems involving elaborate physical, biological, and social phenomena. The project, called “Robust, Interpretable, and Efficient Unsupervised Learning with K-set Clustering,” is expected to have broad applicability in data science.

Modern machine learning techniques aim to design models and algorithms that allow computers to learn efficiently from vast amounts of previously unexplored data, says Balzano. Typically the data is broken down in one of two ways. Dimensionality-reduction uses an algorithm to break down high-dimensional data into low-dimensional structure that is most relevant to the problem being solved. Clustering, on the other hand, attempts to group pieces of data into meaningful clusters of information.

However, explains Balzano, “as increasingly higher-dimensional data are collected about progressively more elaborate physical, biological, and social phenomena, algorithms that aim at both dimensionality reduction and clustering are often highly applicable, yet hard to find.”

Balzano plans to develop techniques that combine the two key approaches used in machine learning to decipher data, while being applicable to data that is considered “messy.” Messy data is data that has missing elements, may be somewhat corrupted, or is filled heterogeneous information – in other words, it describes most data sets in today’s world.

Balzano is an affiliated faculty member of both the Michigan Institute for Data Science (MIDAS) and the Michigan Institute for Computational Discovery and Engineering (MICDE). She is part of a MIDAS-supported research team working on single-cell genomic data analysis.

Read more about the NSF CAREER award…

Sep 18
0

MICDE to provide data analysis and dissemination support for $18 million tobacco research center

By | General Interest, Happenings, News, Research

The University of Michigan School of Public Health will house a new, multi-institutional center focusing on modeling and predicting the impact of tobacco regulation, funded with an $18 million federal grant from the National Institutes of Health and the Food and Drug Administration.

The Center for the Assessment of the Public Health Impact of Tobacco Regulations will be part of the NIH and FDA’s Tobacco Centers of Regulatory Science, the centerpiece of an ongoing partnership formed in 2013 to generate critical research that informs the regulation of tobacco products.

The Michigan Institute for Computational Discovery and Engineering (MICDE) will support the center’s Data Analysis and Dissemination core by collecting national and regional survey data, conducting analysis of the use of tobacco products including vaping and e-cigarettes, and disseminate the resulting tobacco modeling parameters to other research centers and the Food and Drug Administration.

The center is led by MICDE affiliated faculty member Rafael Meza, associate professor of Epidemiology, and David Levy, professor of Oncology at Georgetown University.

For more on the center, see the press release from the U-M School of Public Health: https://sph.umich.edu/news/2018posts/tcors-091718.html

Sep 10
0

U-M part of new software institute on high-energy physics

By | General Interest, Happenings, News, Research

The University of Michigan is part of an NSF-supported 17-university coalition dedicated to creating next-generation computing power to support high-energy physics research.

Led by Princeton University, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will focus on developing software and expertise to enable a new era of discovery at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland.

Shawn McKee, Research Scientist in the U-M Department of Physics, is a co-PI of the institute. His work will focus on integrating and extending the Open Storage Grid networking activities with similar efforts at the LHC.

For more information, see Princeton’s press release, and the NSF’s announcement.

May 07
1

MICDE awards seven Catalyst Grants

By | General Interest, Happenings, News, Research

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum Computing
Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.
Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swim
Researchers: Silas Alben, Mathematics; Robert Deegan, Physics; Alex Gorodetsky, Aerospace Engineering
Description: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification
Researchers: Valeriy Ivanov, Civil and Environmental Engineering; Nikolaos Katopodes, Civil and Environmental Engineering; Darren McKague Climate and Space Sciences and Engineering; Khachik Sargsyan, Sandia National Labs.
Description: The research team will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets
Researchers: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic Inference
Researchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

For more on the Catalyst Grants, see http://micde.umich.edu/catalyst/.

Apr 10
4

Eric Parish, Aero Ph.D student, wins Von Neumann Fellowship from Sandia National Labs

By | Happenings, News, Research

Eric Parish

Eric Parish, who will graduate this spring with a Ph.D in Aerospace Engineering, is the 2018 recipient of the prestigious John von Neumann Postdoctoral Research Fellowship from Sandia National Laboratories (SNL). The highly competitive fellowship offers the opportunity to establish his own program at SNL to conduct innovative research in computational mathematics and scientific computing on advanced computing architectures.

Parish came to U-M from the University of Wyoming, and has developed innovative methodologies of computational math and physics with Prof. Karthik Duraisamy.

Parish said two of his accomplishments in his doctoral work have been developing data-driven solutions to computational physics problems using the NSF-funded ConFlux computing cluster, and bringing together ideas from statistical mechanics to develop efficient numerical solutions of complex partial differential equations.

“It was bridging a gap between communities,” he said of the latter effort.

“Eric came up with a particularly clever way of generalizing concepts from physics to develop a foundation to solve complex equations at a low cost in a mathematically rigorous fashion,” Duraisamy said. “He is one of the rare students who commands an exceptional grasp of applied mathematics, computing and physics, while being well-rounded in his organizational and communication skills. It has been a pleasure and a privilege to work with him.”

Parish said this research could eventually help usher the next generation of flight, for example, “hypersonic” aircraft that can travel at speeds of Mach 8-10. To help get there, his work moves the field toward a better understanding of the underlying physical phenomena via accurate numerical simulations.

At Sandia’s labs in Livermore, Calif., Parish said he plans to continue the work he started at U-M to develop “reduced order models”, which can process past simulation data to greatly reduce the computational cost of future simulations.

Parish said that conducting research at U-M, with the availability of high performance computing resources and a community of computational scientists to bounce ideas off of, helped push his research to a higher level.

“Within Aero, there are five or six very strong computational groups, which really helps me understand the fundamental aspects of what we’re doing, and what the addition of my small little delta means,” he said. “It’s very exciting to do computational research in that environment; it motivates me to come up with better code.”

In 2016, Parish received a $4,000 fellowship from the Michigan Institute for Computational Discovery and Engineering (MICDE). He used some of the funds to attend the International Workshop on Variational Multiscale Methods in Spain last year, where he met a few dozen people from around the world working on similar problems.

“It was fantastic to network and learn from them,” he said.

Parish grew up in Laramie, Wyo., before attending the University of Wyoming, where he played Division 1 golf. He said there was a small but active computational science community at U-W.

“For its size, there was a lot of good computational stuff there,” he said, adding that 10 years ago he would never have predicted the current direction of his career.

Golf played a significant role in his development as well, Parish said: “Being a successful student-athlete takes an extraordinary amount of work. The successes and failures I had … played an integral part in the development of my work ethic, time management skills, mental attitude, and overall growth as a person…I believe that the experience I gained as a student-athlete gave me a unique perspective and skill set that I was able to use to my advantage.”

As far as his future goes after Sandia, Parish said he plans to either continue in the national lab environment or to explore faculty positions so that he can teach and motivate students as his professors at Wyoming and Michigan did for him.

“I’m grateful for everyone’s help,” he said. “The doors that Michigan can open and the quality of people here are very apparent.”

A simulation of magnetohydrodynamic turbulence done on the ConFlux cluster with roughly 1 billion degree of freedom computation generating about 4TB of data.

Jan 11
0

2016-2017 MICDE Research Snapshot

By | Research

2016-2017 has been a year of sustained growth for MICDE’s research portfolio. The number of faculty affiliated with the institute stands at 130, spanning 30 departments and eight schools and colleges. The Center for Scientific Software Infrastructure was established to bring together the U-M community engaged in developing open scientific software. It will focus on establishing best practices for developing, disseminating and documenting scientific software in the public domain. Led by Prof. Emanuel Gull (Physics), the Center aims to provide training and support for researchers that are ready to transform their research codes into well-engineered software. It offers grant support in the form of programmers, consultants, and administrative assistance. It includes a portal to share your code with the research community at large.

MICDE’s two established centers, the Center for Network and Storage-Enabled Collaborative Computational Science (CNSECCS) and the Center for Data-Driven Computational Physics (DaCoP), each held their first symposium, showcasing their first year of research activities. This included evidence of the growing reach of OSiRIS, the open framework for storage, computation and collaboration against big scientific data, and the first results from ConFlux, U-M’s groundbreaking computing cluster for data-driven computational physics. These results have been presented at several conferences, and are appearing in the leading computational journals.

Vorticity field at a late time in the evolution of an elliptic vortex patch computed by a Lagrangian particle method with remeshing and treecode-accelerated evaluation of the Biot-Savart integral. (source: Ling Xu)

MICDE also funded its first round of Catalyst Grants, that are supporting four innovative computational science research projects. Research funded by the Catalyst Grants is breaking new ground, while helping define the future of computational science. This research consists of:

  • studies of the neuronal dynamics of learning and memory formation;
  • new algorithms for the complex, nonlinear dynamics of power grids;
  • novel integral equations methods using recent advances in numerical analysis;  
  • and probabilistic computational frameworks for rare but often catastrophic events.

The past academic year MICDE hosted 14 external speakers with backgrounds and research concentrations that span the breadth of computational science of today and the future. The series culminated in MICDE’s annual symposium: “The New Era of Data-Enabled Computational Science,” which featured talks by worldwide leaders in computational science, including U-M faculty. The symposium included a student poster competition with over 50 entries.

Dr. Ann Almgren from the Lawrence Livermore National Lab speaking about Next Generation AMR, part of the 2016-2017 MICDE Seminar Series

MICDE faculty are committed to growing the already strong U-M community of computational scientists. Over the past year, as before, we have organized a number of workshops to foster collaboration and put together interdisciplinary teams in response to funding calls from federal agencies and foundations.   MICDE offers faculty teams institutional support and direct links to our excellent educational programs as well as cyberinfrastructure, all of which strengthen faculty proposals. With the backing of our parent unit, Advanced Research Computing (ARC), and its technical and consulting services (ARC-Technology Services, and Consulting for Statistics, Computing and Analytics Research), this effort has raised over $22M in external funding over the past 2 years. This includes support from federal agencies (NSF, NIH, and DOD), as well as from industry.  We also work with the academic units at U-M to identify compelling new areas for recruiting the type of faculty members who will drive computational science in the future.

 

Dec 15
0

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether this notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)