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    PinT 2021 – 10th Workshop on Parallel-in-Time Integration

    August 2-6, 2021. PinT 2021 will be offered in a virtual-format. 

    Register online on the Registration Page.

    Computer models and simulations play a central role in the study of complex systems in engineering, life sciences, medicine, chemistry, and physics. Utilizing modern supercomputers to run models and simulations allows for experimentation in virtual laboratories, thus saving both time and resources. Although the next generation of supercomputers will contain an unprecedented number of processors, this will not automatically increase the speed of running simulations. New mathematical algorithms are needed that can fully harness the processing potential of these new systems. Parallel-in-time methods, the subject of this workshop, are timely and necessary, as they extend existing computer models to these next generation machines by adding a new dimension of scalability. Thus, the use of parallel-in-time methods will provide dramatically faster simulations in many important areas, such as biomedical applications (e.g., heart modeling), computational fluid dynamics (e.g., aerodynamics and weather prediction), and machine learning. Computational and applied mathematics plays a foundational role in this projected advancement.

    The primary focus of the proposed parallel-in-time workshop is to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods. This workshop aligns with the National Strategic Computing Initiative (NSCI) objective: “increase coherence between technology for modeling/simulation and data analytics”. The need for parallel time integration is being driven by microprocessor trends, where future speedups for computational simulations will come through using increasing numbers of cores and not through faster clock speeds. Thus as spatial parallelism techniques saturate, parallelization in the time direction offers the best avenue for leveraging next generation supercomputers with billions of processors. Regarding the mathematical treatment of parallel time integrators, one must use advanced methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretization and integration, convergence analyses of iterative methods, and the development and implementation of new parallel algorithms. Thus, the workshop will bring together an interdisciplinary group of experts spanning these areas.


    Article by Tim Havens Published in Acoustical Society Journal


    Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems, has co-authored a paper recently published in The Journal of the Acoustical Society of America, Volume 50, Issue 1.

    The paper is titled, “Recurrent networks for direction-of-arrival identification of an acoustic source in a shallow water channel using a vector sensor.” Havens’s co-authors are Steven Whitaker (EE graduate student), Andrew Barnard (ME-EM/GLRC), and George D, Anderson, US Naval Undersea Warfare Center (NUWC)-Newport.

    The work described in the paper was funded by the United States Naval Undersea Warfare Center and Naval Engineering Education Consortium (NEEC) (Grant No. N00174-19-1-0004) and the Office of Naval Research (ONR) (Grant No. N00014-20-1-2793). This is Contribution No. 76 of the Great Lakes Research Center at Michigan Technological University.

    Abstract

    Conventional direction-of-arrival (DOA) estimation algorithms for shallow water environments usually contain high amounts of error due to the presence of many acoustic reflective surfaces and scattering fields. Utilizing data from a single acoustic vector sensor, the magnitude and DOA of an acoustic signature can be estimated; as such, DOA algorithms are used to reduce the error in these estimations.

    Three experiments were conducted using a moving boat as an acoustic target in a waterway in Houghton, Michigan. The shallow and narrow waterway is a complex and non-linear environment for DOA estimation. This paper compares minimizing DOA errors using conventional and machine learning algorithms. The conventional algorithm uses frequency-masking averaging, and the machine learning algorithms incorporate two recurrent neural network architectures, one shallow and one deep network.

    Results show that the deep neural network models the shallow water environment better than the shallow neural network, and both networks are superior in performance to the frequency-masking average method.

    Citation: The Journal of the Acoustical Society of America 150, 111 (2021); https://doi.org/10.1121/10.0005536Steven Whitaker1,b)Andrew Barnard2George D. Anderson3, and Timothy C. Havens4