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Research Opportunities.

Computational Science Courses

UCSL's ultimate curricular goal is to offer two groups of courses to undergraduates. The first group will focus on the common principles and techniques needed by computational researchers from all participating disciplines. These courses will be modeled after the University of Pittsburgh example5 and include units on computer science, modern programming techniques, alternate programming languages, numerical methods, vector and parallel processing, operating systems, computer architecture, visualization, and networking. The second set of courses will be more discipline-oriented and will apply computational principles and techniques to applications of current interest at UCSL.

As a first step toward this goal we are developing two new courses in each group, supplemented by improvements in two existing courses. The courses will be taught in a lecture - lab format, typically two lectures per week plus two hours of scheduled time in the UCSL seed lab. The lab will also be available to students outside of scheduled class times.

[bullet]Interdisciplinary Methods Courses

Development of two new wide-range interdisciplinary courses with the facilities of the seed lab has begun: Computer Organization and Networking for Scientists and Methods of Computational Science. These courses would be relevant to any student desiring a deeper understanding of computational science, including students in physics, chemistry, math, computer science, geology/geography, and economics.

    [bullet]Computer Organization and Networking for Scientists:

    In order to take maximum advantage of state-of-the-art computer hardware and software, and to write efficient code, computational scientists need a solid understanding of how computers work. The Computer Organization and Networking course will be an introduction to computer hardware, architecture, operating systems, and networks, including units on vector and parallel processing. The course is intended to be introductory in nature, taught at the sophomore/junior level. Emphasis will be on how computer design affects scientific computing.
    [bullet]See course syllabus

    [bullet]Methods of Computational Science:

    This course will introduce students to the basic methods and algorithms of numerical mathematics with strong emphasis on applications in science. Students will also get their first experience with scientific visualization. The course will be taught on the sophomore-junior level, designed to help students develop the computational tools needed to tackle problems in advanced courses.
    [bullet]See course syllabus

[bullet]Computational Physics and Chemistry Courses

We have further improved and upgraded two existing courses: Computational Physics, and Computational Chemistry. With the background gained by students in the above mentioned methods courses, our discipline-specific courses can cover more advanced material.

    [bullet]Advanced Computational Physics:

    This course covers simulation methods that have had a major impact in the discipline. Several modules will be used (depending on instructor) each probing a specific physics problem in some depth with numerical simulation methods. Students are responsible for applying the methods learned in the previous courses to moderately sophisticated physics simulations such as molecular dynamics, monte carlo simulations, fluid dynamics, and cellular automata.
    [bullet]See course syllabus


    [bullet]Advanced Computational Chemistry:

    This course focusses on current topics in computational chemistry. Techniques covered include molecular mechanics, along with ab initio and semiempirical methods of electronic structure theory. Depending on the instructor, other topics, such as molecular reaction dynamics or the electronic structure of solids, may be included as well.
    [bullet]See course syllabus

[bullet]Advanced Topics Courses

Finally, two new advanced courses are under development specifically for Physics and Chemistry students: Nonlinear Science, and Molecular Dynamics. These courses are designed for students interested in pursuing in depth studies of these topics, and would be particularly useful for students interested in an undergraduate research project with faculty members specializing in these areas and for students intending to pursue computational science in graduate school.

    [bullet]Molecular Dynamics:

    This course provides a hands on introduction to this widely used simulation method used to study molecular motion and molecular interactions. Examples are drawn from physics, chemistry, and biophysics - all fields in which molecular dynamics has made significant contributions.
    [bullet]See course syllabus

    [bullet]Nonlinear Science:

    Perhaps no area of science has benefited more from the advances in computing than those under the umbrella of ''nonlinear'' science. The Nonlinear Science course presents an introduction to nonlinear dynamics, dynamical systems, and chaos, with applications from physics, chemistry, and biology. Hands on experience with computational techniques is stressed and relevant examples from faculty research will be used.
    [bullet]See course syllabus

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