Spring 2021 Courses

ISC 1057
Computational Thinking
Sachin Shanbhag
This introductory course considers the question of how computers have come to imitate many kinds of human intelligence. The answer seems to involve our detecting patterns in nature, but also in being able to detect patterns in the very way we think. We will look at some popular computational methods that shape our lives, and try to explain the ideas that make them work. This course has been approved to satisfy the Liberal Studies Quantitative/Logical Thinking requirement.
ISC 2310
Introduction to Computational Thinking in Data Science with Python
Janet Peterson
This course investigates strategies behind popular computational methods used in data science. In addition, many of the algorithms are implemented using the programming language Python. No prior programming experience is required so the course presents the basics of the Python language as well as how to leverage Python’s libraries to solve real-world problems in data science. Prerequisite: MAC 1105 or equivalent.
ISC 4220C
Continuous Algorithms for Science Applications
M W F 9:20-10:10, T 3:05-5:35 (Lab), REMOTE
Sachin Shanbhag
Basic computational algorithms including interpolation, approximation, integration, differentiation, and linear systems solution presented in the context of science problems. The lab component includes algorithm implementation for simple problems in the sciences and applying visualization software for interpretation of results. Corequisite: ISC 3222; Prerequisite: MAC 2312.
ISC 4304C
Programming for Science Applications
T R 9:45-11:00, M 3:05-5:35 (Lab), REMOTE
Peter Beerli
Provides knowledge of a scripting language that serves as a front end to popular packages and frameworks, along with a compiled language such as C++. Topics include the practical use of an object-oriented scripting and compiled language for scientific programming applications. There is a laboratory component for the course; concepts learned are illustrated in several science applications. Prerequisites: MAC 2311, COP 3014 or ISC 3313.
ISC 4933/5227
Survey of Numerical Partial Differential Equations
T R 11:35-12:50, REMOTE
Tomasz Plewa
This course provides an overview of the most common methods used for numerical partial differential equations. These include techniques such as finite differences, finite volumes, finite elements, discontinuous Galerkin, boundary integral methods, and pseudo-spectral methods.
ISC 4933/5238C
Scientific Computing for Integral Equation Methods
M W F 1:20-2:10, REMOTE
Bryan Quaife
This course covers key algorithms that are required when solving integral equations. Prerequisites: MAD 3703 and MAP 4341; ISC 4232; or instructor permission.
ISC 4933/5318
High-Performance Computing
M W F 10:40-11:30, REMOTE
Xiaoqiang Wang
Introduces high-performance computing, which refers to the use of parallel supercomputers, computer clusters, as well as software and hardware to speed up computations. Students learn to write faster code that is highly optimized for modern multi-core processors and clusters, using modern software development tools and performance analyzers, specialized algorithms, parallelization strategies, and advanced parallel programming constructs. Prerequisite: ISC 5305 or equivalent or instructor permission.
ISC 5935
Uncertainty Quantification in the PDE Setting
T R 3:05-4:20, REMOTE
Max Gunzburger
Quantifying uncertainties in systems governed by partial differential equations is an important endeavor in simulation, design, and control in all areas of science and engineering. Monte Carlo methods are in very common use but are limited by their high cost. Alternatives methods are discussed that reduce costs by lowering the number and/or cost of the PDE solves used. The course is self contained: short presentations about PDEs, their finite element approximation, and notions from probability are discussed.
ISC 4943
Practicum in Computational Science
T R 1:20-2:35, REMOTE
Anke Meyer-Baese
This practicum allows students to work individually with a faculty member throughout the semester and meet with the course instructor periodically to provide progress reports. Written reports and an oral presentation of work are required. May be repeated to a maximum of six semester hours, with a maximum of only three semester hour credits allowed to be applied to the Computational Science degree.
ISC 5316
Applied Computational Science II
T R 9:30-10:45, R 3:30-6:00 (Lab), REMOTE
Tomasz Plewa
Provides students with high performance computational tools to investigate problems in science and engineering with an emphasis on combining them to accomplish more complex tasks. Topics include numerical methods for partial differential equations, optimization, statistics, and Markov chain Monte Carlo methods. Prerequisite: ISC 5315.
ISC 5473
Introduction to Density Functional Theory
T R 1:20-2:35, REMOTE
Chen Huang
For materials scientists, chemists, physicists, and applied mathematicians who want to know both the basic concept and certain advanced topics in density functional theory. Density functional theory is widely used in both industry and academia to simulate various properties of materials and molecules, such as electronic properties, crystal structures, and chemical reactions. We will learn how to solve realistic materials problems using density functional theory and the underlying theories.
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