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Gillman, Adrianna Marie

Associate Professor

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Research

research overview

  • Adrianna Gillman's research focuses on developing efficient, accurate and robust numerical methods for approximating solutions to partial differential equations. The algorithms she develops allows users to simulate physical phenomena on a desktop computer that is regularly shipped out to super computer. Her research specialties include scattering, imaging, and fluid simulations.

keywords

  • numerical linear algebra, numerical partial differential equations, integral equations, high order discretizations

Publications

selected publications

Teaching

courses taught

  • APPM 1360 - Calculus 2 for Engineers
    Primary Instructor - Spring 2020
    Continuation of APPM 1350. Focuses on applications of the definite integral, methods of integration, improper integrals, Taylor's theorem, and infinite series. Degree credit not granted for this course and MATH 2300.
  • APPM 4600 - Numerical Methods and Scientific Computing
    Primary Instructor - Fall 2022 / Fall 2023 / Fall 2024
    Provides an introduction to numerical analysis and scientific computing. Numerical analysis topics include root finding, interpolation, quadrature, linear system solution techniques, and techniques for approximating eigenvalues. Scientific computing topics include code development and repository management in addition to an introduction to shared and distributed memory computing. Involves hands-on learning with weekly group interactions and a final project including a report and in-class presentation. Recommended prerequisite: knowledge of a programming language such as Python, and C++.
  • APPM 4610 - Numerical Differential Equations
    Primary Instructor - Spring 2023 / Spring 2024
    Provides an introduction to the most commonly used techniques for numerically solving boundary value problems and time dependent problems and the corresponding linear systems. Topics include finite difference methods, the finite element method, the spectral method, spectral collocation methods, Euler and Runge-Kutta methods. Scientific computing skills such as advanced code and memory management will be developed. Involves hands-on learning with weekly group interactions and a final project. Department enforced prerequisite: Knowledge of a programming language such as Python, and C++ is required.
  • APPM 4650 - Intermediate Numerical Analysis 1
    Primary Instructor - Fall 2019 / Fall 2021
    Focuses on numerical solution of nonlinear equations, interpolation, methods in numerical integration, numerical solution of linear systems, and matrix eigenvalue problems. Stresses significant computer applications and software. Department enforced prerequisite: knowledge of a programming language. Same as MATH 4650.
  • APPM 4660 - Intermediate Numerical Analysis 2
    Primary Instructor - Spring 2021 / Spring 2022
    Continuation of APPM 4650. Examines numerical solution of initial-value problems and two-point boundary-value problems for ordinary differential equations. Also looks at numerical methods for solving partial differential equations. Department enforced prerequisite: knowledge of a programming language. Same as MATH 4660.
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