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Courses - Spring 2019
AMSC
Applied Mathematics & Scientific Computation Department Site
AMSC661
Scientific Computing II
Credits: 3
Grad Meth: Reg
Prerequisite: Must have knowledge of C or Fortran. And AMSC460 or CMSC460; or (CMSC466 or AMSC466); or (must have knowledge of basic numerical analysis (linear equations, nonlinear equations, integration, interpolation); and permission of instructor).
Also offered as: CMSC661.
Credit only granted for: AMSC661 or CMSC661.
Fourier and wavelet transform methods, numerical methods for elliptic partial differential equations, numerical linear algebra for sparse matrices. Finite element methods, numerical methods for tiem dependent partia l differential equations. Techniques for scientific computation with an introduction to the theory and software for each topic. Course is part of a two course sequence (660 and 661), but can be taken independently.
AMSC664
(Perm Req)
Advanced Scientific Computing II
Credits: 3
Grad Meth: Reg
Prerequisite: AMSC663.
Restriction: Permission of instructor.
Also offered as: CMSC664.
Credit only granted for: AMSC664 or CMSC664.
In the sequence AMSC 663, AMSC 664 students work on a year-long individual project to develop software for a scientific task in a high performance computing environment. Lectures will be given on code development and validation, parallel algorithms for partial differential equations, nonlinear systems, optimization.
AMSC715
Numerical Methods for Evolution Partial Differential Equations
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: Permission of instructor; or one graduate level course in partial differential equations or one graduate level course in numerical analysis or scientific computing.
Credit only granted for: AMSC612 or AMSC715.
Formerly: AMSC612.
Additional information: This course continues AMSC 714, but can be taken independently, and is a complement to the graduate courses MATH 673 and MATH 674 in PDEs, AMSC 666 in numerical analysis, and AMSC 660 and AMSC 661 in scientific computing.
Topics include: Heat and wave equations: maximum principle, energy methods and Sobolev spaces, finite difference and finite element methods, von Neumann analysis, stability and error estimates;Linear first order PDEs: upwinding and monotone schemes, finite difference, finite volume, and discontinuous Galerkin methods; Nonlinear conservation laws: weak solutions and entropy conditions,monotone methods.
AMSC760
(Perm Req)
Applied Statistics Practicum
Credits: 3
Grad Meth: Reg
Prerequisite: Must have completed one year of graduate study in Applied Statistics.
Restriction: Must have project proposal approved by SAC coordinator.
A semester long applied applied statistical project (a minimum 10 hours per week or 120 hours in total), in an internship of collaborative research-laboratory setting working on a substantive applied quantitative project with significant statistical content.
AMSC762
(Perm Req)
Data Analysis Project
Credits: 1
Grad Meth: Reg
Restriction: Permission of CMNS-Applied Mathematics department; and permission of instructor.
This course cannot be used to meet any of the Applied Statistics Area's seminar requirements. Offered yearly, required of and limited to MS non-thesis and doctoral students in Applied Statistics Area, for whom the resulting projects serve as a Qualifying Exam component. After 5-6 lectures or presentations on components of successful data analyses and write-ups, 3-4 sessions will discuss previous student project submissions. The culminating project, to be completed in a two week period between semesters, is an analysis and written report of one of three project choices made available each year to represent a spectrum of realistic applied statistical problems.
AMSC764
Advanced Numerical Optimization
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: MATH410; or permission of instructor.
Also offered as: CMSC764.
Credit only granted for: AMSC607, AMSC764 or CMSC764.
Formerly: AMSC607.
Modern numerical methods for solving unconstrained and constrained nonlinear optimization problems in finite dimensions. Design of computational algorithms and the analysis of their properties.