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The University of Tennessee

IGMCS: Interdisciplinary Graduate Minor in Computational Science

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Courses for Spring 2008


IGMCS: Course Offerings for Fall 2008

Fall '08 course offerings are listed below. Please check with your home department, as well as the latest Graduate Catalog and Academic Calendar, to verify course availability.

Departments

Biochemistry & Cellular and Molecular Biology


Departmental Liaison: Dr. Cynthia Peterson (cbpeters@utk.edu)

Courses

401 Biochemistry-Molecular Biology I (4)
First semester of a two course sequence providing in-depth coverage of biochemistry and molecular biology. Covers amino acid structure and chemistry, protein structure and chemistry, protein folding, enzyme behavior and function, reaction mechanisms, catabolism and energy transfer, synthetic metabolism including photosynthesis, and protein transport.
(DE) Prerequisite(s): Biology 240 and Chemistry 350, 360, and 369.

402 Biochemistry-Molecular Biology II (4)
Second semester of a twocourse sequence providing in-depth coverage of biochemistry and molecular biology. Covers structure of DNA and RNA, experimental methods of analyzing nucleic acids, mechanisms of RNA and protein synthesis, mechanisms of DNA replication, repair and recombination, chromosome structure and function, regulation of gene expression, genome structure and genomics, and mechanisms of biological regulation.
(DE) Prerequisite(s): Biology 240 and Chemistry 350, 360, and 369.

471 Biophysical Chemistry (3)
Physicochemical principles with applications to biological systems. Thermodynamics; chemical equilibrium; solution chemistry; transport; electrochemistry; kinetics; enzyme catalyzed reactions. (Same as Chemistry 471.)
(DE) Prerequisite(s): Chemistry 350 and 360, Mathematics 125, and general biology or consent of instructor.

510 Computational Structural Biochemistry (1)
Computational approaches to biomolecular structure, including homology modeling, threading, and molecular dynamics. (DE) Prerequisite(s): Prior knowledge of cell biology and biochemistry.
Registration Permission: Consent of instructor.

511 Advanced Protein Chemistry and Cellular Biology (3)
Cellular structure and function at molecular and supramolecular level in progression: protein structure and function; membrane structure and function; bioenergetics and membrane proteins.
(DE) Prerequisite(s): Prior knowledge of cell biology and biochemistry. Registration Permission: Consent of instructor.

515 Experimental Techniques I (2-4)
Introduction to modern experimental methodology and instrumentation in biochemistry, molecular biology and cell biology, including cell culture; spectrophotometry; microscopy; nucleic acid purification and analysis; protein assays; enzyme purification; electrophysiology; computer analysis of nucleic acid and protein sequences. Team-taught lecture/demonstration format. Repeatability: May be repeated. Maximum 6 hours. Comment(s): Primarily for departmental graduate students.


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Chemistry


Departmental Liaison: Dr. Robert Hinde (rhinde@utk.edu)

Courses

Chemistry 570: Quantum and Computational Chemistry.
This course focuses on the time-independent Schroedinger equation and its applications in molecular quantum mechanics. Particular emphasis is given to ab initio electronic structure computations including Hartree-Fock molecular orbital theory and post-Hartree-Fock treatment of electron correlation in atoms and molecules. As part of this course, students typically perform several ab initio electronic structure computations for small molecules using the GAMESS or NW-Chem suite of electronic structure computer programs. This course has been taught by Prof. Robert Harrison and by Prof. Robert Hinde (individually) in recent years, and is typically offered every fall semester. Students with a full year of undergraduate physics, undergraduate chemistry, and undergraduate calculus, and some exposure to differential equations, are likely to succeed in this course.


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Chemical Engineering


Departmental Liaison: Dr. David Keffer (dkeffer@utk.edu)

Courses

ChE/MSE 505 Advanced Mathematics for Engineers (3)
This is a practical problem-solving course designed to prepare an individual with a repertoire of analytical and numerical tools to solve a broad swath of mathematical problems. We focus on solutions to both single equations and systems of equations, both linear and nonlinear. We address the following types of equations: algebraic equations, ordinary differential equations, parabolic, hyperbolic and elliptic partial differential equations, and integral equations. Our approach is to examine analytical solutions where available then move to numerical solutions. For each type of numerical algorithm, we present examine the advantages and disadvantages of the approach.


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Earth and Planetary Sciences


Departmental Liaison: Dr. Edmund Perfect (eperfect@utk.edu)

Courses

Geology 501 – Fractal Models in Earth Sciences (3 credits, Masters and Ph.D.)
An introduction to the theory and methods of fractal analysis as applicable to earth sciences. Topics include deterministic and statistical fractals, self-affine fractals, multifractals, percolation, renormalization group theory, cellular automata, and methods of estimating fractal parameters (e.g., dimension and lacunarity). Applications to be discussed include: characterization of coastlines, drainage basins, and fracture networks; terrain simulation; modeling porous media and hydraulic properties; rock fragmentation; spatial variability of mineral deposits; and temporal variability of earthquakes and floods.
Recommended background: 6-8 hours of coursework in earth sciences, calculus, or consent of instructor.

Geology 685 – Seminar in Hydrogeology (3 credits, Masters and Ph.D.)
Advanced treatment of selected topics in hydrogeology.
Registration permission: consent of instructor.


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Ecology & Evolutionary Biology


Departmental Liaison: Dr. Lou Gross (gross@tiem.utk.edu)

Courses

EEB 581-2 (also Math 581-2): Mathematical Ecology (3 credits each semester) 581 Fall '08

Deterministic and stochastic models in ecology with computational projects. Requires advanced undergrad math background (e.g. calculus, DE, linear algebra, Advanced DE or Advanced Calculus)

EEB 681-2 (also Math 681-2): Advanced Mathematical Ecology (3 credits each semester) 681 Fall '08
Selected topics in computational and theoretical modeling in ecology, topics including epidemiology, spatial modeling, ecotoxicology and resource management. Requires Math 581-2.
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Electrical Engineering and Computer Science


Departmental Liaison: Dr. Jack Dongarra (dongarra@eecs.utk.edu)

Courses

CS 594 Special Topics in Computer Science - The following are preliminary courses to be offered under 594:

Biologically Inspired Computation (3)
A course that explores information processing and self-organization in biological systems. Topics include dynamical systems concepts (attractors, basins of attraction, Wolfram classes, stability, Lyapunov functions, information theory, thermodynamic limits of computation), cellular automata (Langton's lambda, phase transitions, computation and life at the "edge of chaos"), and excitable media (cardiac tissue, slime mold, reaction-diffusion systems, activator-inhibitor systems, Turing patterns and animal hair-coats), just to name a few. Students' understanding of complex systems and dynamical processes is enhanced by videos of biological systems and in-class demonstrations and experiments using multi-agent simulations.
Prerequisites: basic programming ability, linear algebra (e.g., Math 251), differential equations (e.g., Math 231, 241, but primarily just the concepts of differential equations and partial derivatives), probability and statistics (e.g., Math 323). Basic biology and physics are helpful as well.

Data Mining (3)
A comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling, association analysis, clustering, classification, and anomaly detection. Prereq: Discrete mathematics or statistics and programming.

Genome Science and Technology


Departmental Liaison: Dr. Cynthia Peterson (cbpeters@utk.edu)

Courses

507 Bioinformatics and Computational Biology (1-3)
Topics to be covered include the application of computing, modeling, data analysis, and information technology to fundamental problems in the life sciences. Repeatability: May be repeated. Maximum 12 hours.

510 Special Topics in Life Sciences (1-3)
Specializations in biotechnology; cellular, molecular, and developmental biology; environmental toxicology; ethology; plant, physiology and genetics; and physiology. Repeatability: May be repeated. Maximum 9 hours.

520 Genome Science and Technology I (4)
Overview of genomics, advanced genetics principles.

595 Special Topics in Genome Science and Technology (1-3)
Tutorials or lectures in variety of special topics to be chosen by instructor. Repeatability: May be repeated. Maximum 12 hours.

695 Advanced Topics in Genome Science and Technology (1-3)
Tutorials or lectures on variety of advanced topics to be chosen by instructor. Repeatability: May be repeated. Maximum 12 hours.


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Geography


Departmental Liaison: Dr. Bruce Ralston (bralston@utk.edu)

Courses

M.S. Level: 411 Introduction to Geographic Information Science (3)
Concepts and methods of spatial analysis and their application to geographic information systems software and techniques. Emphasizes both theoretical and applied aspects of GIS. 2 hours lecture and 2 hours lab. Prereq: Geography 310 or consent of instructor.

414 Spatial Databases and Data Management (3)
Types, sources, acquisition, and documentation of spatial data. Spatial database management methods and strategies for data sharing. 2 hours lecture and 2 hours lab. Prereq: 411.

517 Geographic Information Management and Processing (3)
Concepts and methods in management of geographic information. Database desgin, manipulation, sampling and analysis. Prereq: consent of instructor.


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Information Science


Departmental Liaison: Dr. Peiling Wang (peilingw@utk.edu)

Courses

Domain Science/Software

IS 584 Database Management Systems (3)
Defining data needs, data structures, role of operating systems in data management, file organization, database management systems, logical data models, internal data models, database administration and evaluation. Design and implementation of application using database management system.

Hardware/Software

IS 585 Information Technologies (3) Section 001 PC Only (Web-based course requires access to a PC)
Evolution, trends, capabilities, and limitations of technologies applied to information capture, storage, preservation, access, and distribution.

IS 594 Graduate Research Participation (3)
Advanced research techniques under supervision of faculty member whose area coincides with interests of the student. Prereq: Consent of advisor and research director. May be repeated. Maximum 6 hours. Satisfactory/No Credit grading only.

IS 599 Practicum (3-6)
Opportunity to translate theory into practice under guidance of qualified information professionals. Prereq: Completion of required and pertinent advanced courses relevant to student’s practicum design. Minimum 3.0 cumulative GPA. Written consent of advisor and approval of practicum coordinator. May be repeated. Maximum 6 hours. S/NC only.
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Mathematics


Departmental Liaison: Dr. Chuck Collins (ccollins@math.utk.edu)

Courses

We've divided the math courses into three categories:

Mathematics for Modeling - basic mathematics courses covering the content needed to develop and understand mathematical models. Theses courses typically do not have any computation nor much application content.

Mathematical Modeling - courses that develop and solve mathematical models associated with some application area. These courses focus on the modeling process.

Numerical Analysis - courses that develop and analyze the algorithms used to solve specific mathematical problems. Typically these courses have some computational content (projects).

Unless otherwise stated, the pre-requisites for a graduate student to take these courses would be sophomore level mathematics: 241 Calculus III, 231 Differential Equations I & 251 Matrix Algebra I, or their equivalent.

Mathematics for Modeling

Math 453 Matrix Algebra II (3)
Basic matrix algebra, including eigenvalues and eigenvectors. Contains some applications (depending on the instructor) and typically does not address any computational issues. We try to offer this every semester including summer but, due to low enrollment, it may not always run.

Mathematical Modeling

Math 411 Mathematical Modeling (3)
Basics of mathematical modeling covering continuous and discrete (in time) models and stochastic models. Emphasis on the modeling cycle and projects. The basic mathematics is covered as needed. Offered in the Spring.

Numerical Analysis - Basic (these all require some programming skill)

Math/CS 471 Numerical Analysis (3)
Covers interpolation and approximation of functions by polynomials & splines, numerical integration and numerical solution of ODEs. Offered in the Fall.

Math/CS 571 Numerical Mathematics I (3)
Covers the theory for direct and interative methods for solving linear systems of equations, methods for finding eigenvalues, and methods for solving nonlinear systems of equations. Requires some background in analysis as the course emphasises proofs. Offered in the Fall.

Math 578 Numerical Solution of PDEs (3)
Covers numerical approximation of PDEs, especially conservation laws, and the methods used to solve the approximation. Includes some discussion of modeling and implementation. Requires a course in PDEs (435 or 512). Offered in the Fall, every other year.

Math 679 Wavelets, Fast Algorithms, and PDEs (3)
This course introduces and applies fast multiscale and multiresolution methods (e.g., fast multipole methods, wavelets, local Fourier basis, etc) to solve problems which are common in science and engineering. These techniques are a part of real analysis based algorithms. Requires Some familiarity with (lower) graduate level Fourier analysis, partial differential equations, numerical analysis or signal analysis is required; or with instructor’s consent. Working knowledge of one of the programming languages (C, C++, FORTRAN, and Python) is also required. Offered in the Spring.
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Mechanical, Aerospace and Biomedical Engineering


Departmental Liaison: Dr. A. J. Baker (ajbaker@utk.edu)

Courses

Engineering Science 551: Finite Elements for Engineering Applications (3)
Modern computational theory applied to conservation principles across the engineering sciences. Weak forms, extremization, boundary conditions, discrete implementation via finite element, finite difference, finite volume methods. Asymptotic error estimates, accuracy, convergence, stability. Linear problem applications in 1, 2 and 3 dimensions, extensions to non-linearity, non-smooth data, unsteady, spectral analysis techniques, coupled equation systems. Computer projects in heat transfer, structural mechanics, mechanical vibrations, fluid mechanics, heat/mass transport. (Same as Aerospace Engineering 571; Biomedical Engineering 561; Mechanical Engineering 561.) Comment(s): Bachelor\u2019s degree in engineering or natural science required.

Engineering Science 552: Computational Fluid-Thermal Systems (3)
Modern approximation theory applied to incompressible-thermal flows. Navier-Stokes equations, well-posedness, boundary conditions, non-dimensional groups, conjugate heat transfer, algebraic/differential closure models for turbulence. Weak forms, extremization, finite element/finite volume discrete implementations, a priori error estimates, accuracy, convergence, stability. Numerical linear algebra, sparse matrix methods. Applications in boundary layers, streamfunction-vorticity, pressure projection, free-surface, pseudo- compressibility completion theories. Solution-adaptive h- and r-meshing, optimal solution estimates. Augmentation theories for stability, numerical diffusion, Fourier spectral analyses, optimal forms. Computer projects. (Same as Aerospace Engineering 572; Biomedical Engineering 562; Mechanical Engineering 562.) (DE) Prerequisite(s): 551.

Engineering Science 651: Advanced Topics in Computational Fluid (3)
Dynamics (3) Modern approximation theory for Euler and Navier-Stokes conservation systems, compressible flow, hyperbolic forms, boundary conditions. Weak forms, extremization, finite element/finite volume/flux vector discrete implementations, a priori error estimates, accuracy, convergence, stability. Numerical linear algebra, approximate factorization, sparse matrix methods. Dissipation, Fourier spectral analysis, smooth and non-smooth solutions. (Same as Aerospace Engineering 661; Mechanical Engineering 651.) (DE) Prerequisite(s): 552.

Mechanical Engineering 525: Combustion and Chemically Reacting Flows I (3)
Fundamentals: thermochemistry, chemical kinetics and conservation equations; phenomenological approach to laminar flames; diffusion and premixed flame theory; single droplet combustion; deflagration and detonation theory; stabilization of combustion waves in laminar streams; flammability limits of premixed laminar flames; introduction to turbulent flames. (DE) Prerequisite(s): 522 and 541 or consent of instructor.


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Physics


Departmental Liaison: Dr. Thomas Papenbrock (tpapenbr@utk.edu)

Courses

Physics 513-514 Problems in Theoretical Physics (3,3) 513 Fall '08
Fundamentals of physics: classical mechanics (Newtonian mechanics, Lagrangian and Hamiltonian dynamics), electrostatics, magnetostatics, electrodynamics, relativity, and quantum mechanics.

Physics 571-572 Mathematical Methods in Physics (3,3) 571 Fall '08
Linear vector spaces, matrices, tensors, curvilinear coordinates, functions of a complex variable, partial differential equations and boundary value problems, Green’s functions, integral transforms, integral equations, spherical harmonics, Bessel functions, calculus of variations. Prereq: Advanced calculus and differential equations. Must be taken in sequence. (Same as Mathematics 517-518.)


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Statistics


Departmental Liaison: Dr. Hamparsum Bozdogan (bozdogan@utk.edu)

Courses

M.S. Level: 563 Introduction to Mathematical Statistics (3)
Basic probability models and theory of distributions of random variables.
Prereq: Mathematics 241.

572 Applied Regression Analysis (3)
Simple linear regression. Matrix approach to multiple linear regression. Partial and sequential sums of squares, interaction and confounding, use of dummy variables, model selection. Leverage, influence and collinearity. Autocorrelated errors. Generalized linear models, maximum likelihood estimation, logistic regression, analysis of deviance. Nonlinear models, inference, ill-conditioning. Robust regression, M-estimators, iteratively reweighted least squares. Nonparametric regression, kernel, splines, testing lack of fit.
Prereq: 571 and matrix algebra.

579 Applied Multivariate Methods (3)
Multivariate techniques: Hotellings T-sq. MANOVA, discriminant analysis, canonical correlation, principal component analysis, and factor analysis. Computer oriented approach: analysis and interpretation. Knowledge of basic matrices and SAS essential.
Prereq: 538 or knowledge of regression and analysis of variance.

Ph.D. Level: 662 Computational Methods in Statistics (3)
Up-to-date computational methods in statistics: open architecture interactive computational languages supplemented by other statistical packages with graphical capabilities. Statistical computing, numerical methods for linear models and generalized linear models, nonlinear statistical methods, matrix computations and special matrices, essentials of Monte Carlo simulation, and resampling techniques.
Prereq: Knowledge of programming language and 572 or consent of instructor.


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Below is information for academic departments interested in joining the IGMCS program:

Academic departments with existing or planned graduate degree programs are invited to submit requests for participation to the Program Committee. Applications should indicate which degree program options (eg., Masters and/or PhD) are to be included and which courses are to be accepted for each of the options. It is expected that courses will generally be equivalent to existing graduate level courses in the participating departments. The Program Committee representative (College Representative) from the applicant's college may assist in developing the application.

Suggested program modifications that have been approved by the faculty of the participating academic unit should be sent to the College Representative, who in turn will bring them to the attention of the Program Committee for final approval.

The policies and operational guidelines approved by the Faculty Senate for the IGMCS are flexible so that approval for new programs or modification of existing ones can be given with a minimum of delay. Interested students can be admitted provisionally to the IGMCS program at the same time that the sponsoring department is applying for approval of its degree program.

For more information, contact Dr. Jack Dongarra (dongarra@cs.utk.edu).

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Claxton Complex

Contact IGMCS

413 Claxton Complex
Knoxville, TN 37996-3450
Phone: (865) 974-8295
Fax: (865) 974-8296
Email: info@igmcs.utk.edu

Sponsored by CITR