Andrew Kirby, Ph.D.

University of Wyoming, School of Computing, Scientific Simulations. Computational Research Scientist.

prof_pic.jpg

Welcome to my page! I’m an Associate Research Scientist in the School of Computing at the University of Wyoming. Currently, I’m leading computational research projects in the fields of Wind Energy and Aerospace, including work with NREL and Scientific Simulations LLC. In the past, I worked at MIT Lincoln Laboratory in the Supercomputing Group (LLSC), directed by Dr. Jeremy Kepner, as a Postdoctoral Associate developing advanced parallel algorithms for Deep Learning.

I did my Ph.D. in Mechanical Engineering at the University of Wyoming under the direction of Professor Dimitri Mavriplis where I worked on the development of high-order numerical methods for multiscale computational fluid dynamics problems. During my Ph.D. studies, I was fortunate to be selected as an NSF Blue Waters Graduate Fellow, in which I performed the highest-fidelity blade-resolved wind farm simulations to date on leadership-class supercomputers! Additionally, I spent several months working with the U.S. ARMY’s CREATE-AV HELIOS Team at NASA Ames Research Center on my doctoral research. Before changing to the dark side of computational mathematics and engineering, I did a M.S. at Columbia University in Applied Mathematics, and a B.S. in Mathematics at the University of Wisconsin-Madison. During my time at those universities, I got to work with some wonderful people including Marc Spiegelman (APAM Columbia) and Jean-Luc Thiffeault (Math UW-Madison).

Selected Publications

  1. RANS and Hybrid RANS-LES Results for the Fourth High-Lift Prediction Workshop using the NSU3D Solver
    Dimitri J Mavriplis, Mark Bogstad, and Andrew C Kirby
    AIAA Aviation 2022 Forum Jun 2022
  2. Comparison of Propeller-Wing Interaction Simulation using Different Levels of Fidelity
    Zhi Yang, Andrew C Kirby, and Dimitri J Mavriplis
    AIAA SciTech 2022 Forum Jan 2022
  3. Gpu-accelerated discontinuous galerkin methods: 30x speedup on 345 billion unknowns
    Andrew C Kirby, and Dimitri J Mavriplis
    2020 IEEE High Performance Extreme Computing Conference (HPEC) Sep 2020
  4. Layer-parallel training with gpu concurrency of deep residual neural networks via nonlinear multigrid
    Andrew Kirby, Siddharth Samsi, Michael Jones, and 3 more authors
    2020 IEEE High Performance Extreme Computing Conference (HPEC) Sep 2020
  5. Wind farm simulations using an overset hp-adaptive approach with blade-resolved turbine models
    Andrew C Kirby, Michael J Brazell, Zhi Yang, and 5 more authors
    The International Journal of High Performance Computing Applications Sep 2019