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Courses - Spring 2019
Applied Mathematics & Scientific Computation Department Site
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).
Cross-listed with CMSC66 1.
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.
(Perm Req)
Advanced Scientific Computing II
Credits: 3
Grad Meth: Reg
Prerequisite: AMSC663 or CMSC663.
Restriction: Permission of instructor.
Cross-listed with CMSC664.
Credit only granted for: AMSC664 or CMSC664.
In the sequence Advanced Scientific Computing I & Advanced Scientific Computing II, (AMSC663/CMSC663 and AMSC664/CMSC664, respectively) 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 available computational environments, code development, implementation of parallel algorithms.
Partial Differential Equations II
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: MATH673 or AMSC673; or permission of instructor.
Also offered as: MATH674.
Credit only granted for: AMSC674 or MATH674.
Boundary value problems for elliptic partial differential equations via operator-theoretic methods. Hilbert spaces of functions. Duality, weak convergence. Sobolev spaces. Spectral theory of compact operators. Eigenfunction expansions.
Offered Spring only.
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.
(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.
(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.
Advanced Numerical Optimization
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: MATH410; or permission of instructor.
Cross-listed with C MSC764.
Credit only granted for: AMSC607, AMSC764 or CMSC764. F ormerly: 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.