Victoria E. Howle
Associate Chair for Graduate & Postdoctoral Research
Department of Mathematics & Statistics
Texas Tech University
Office: Mathematics 117-D
Phone: (806) 834-8770
My research is in applied mathematics with a focus mainly on numerical linear algebra. My main research interests are currently in physics-based preconditioning for incompressible fluid flow problems, scalable preconditioners for implicit Runge-Kutta methods. I am also recently starting some work in machine learning and quantum computing.
Preconditioning for incompressible fluid flow problems: Along with a number of collaborators, I have worked on developing and analyzing scalable preconditioners for Navier–Stokes simulations, including problems that are extensions to standard incompressible flow models. Many important scientific systems require solution of such extensions, whether by incorporating additional nonlinear effects or by coupling to other physical processes.
Scalable Preconditioners for Implicit Runge–Kutta Methods: Another research area has been investigating scalable preconditioners for implicit Runge–Kutta methods. Many important engineering and scientific systems require the solution of time-dependent PDE systems. Many of these sytems have specific stability needs in order to compute realistic solutions. Certain classes of implicit Runge–Kutta (IRK) methods provide this stability. However, one price of using IRK methods is the need to solve large linear systems at each time step, which can be quite expensive computationally. Preconditioning is necessary for these systems to be solved efficiently.
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Last updated: September 2019
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