
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 disciplineoriented 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.
Interdisciplinary
Methods Courses
Development of two new widerange 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.
Computer
Organization and Networking for Scientists:
In order to take maximum advantage of stateoftheart 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.
 See
course syllabus
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 sophomorejunior level, designed to help
students develop the computational tools needed to tackle problems in
advanced courses.
 See
course syllabus
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 disciplinespecific courses
can cover more advanced material.
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.
 See
course syllabus
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.
 See
course syllabus
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.
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.
 See
course syllabus
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.
 See
course syllabus
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