2000 Physics Chairs Conference
Computational Physics in the Undergraduate
Curriculum
Viewgraphs from the
2000 Physics Department Chairs Conference
April 15, 2000
Harvey Gould
Clark University
collaborator: Jan Tobochnik, Kalamazoo College
support: National Science Foundation
Viewgraphs: Punchlines, Computer Simulation Laboratory, Which Programming Language?, Gordon Research Conference, Statistical and Thermal Physics Curriculum Development Project, Resources
Punchlines
- Changes in the curriculum and innovations in educational
methods should be guided by developments in research.
- Computation has led to important conceptual advances and new
ways of thinking about physical systems which should be reflected
in the curriculum.
- Our goal should be to incorporate computational methods into
the curriculum rather than computers in the classroom.
- The most efficient way to introduce computational physics
into the undergraduate curriculum is in a separate course.
- Computer simulations rather than numerical methods should be
emphasized.
- If students have the needed skills, they will help reform
the curriculum.
- Computing is not a substitute for thinking. Computational
physics does not yield instant gratification as found in many
other computer applications. We need to provide students
opportunities to learn that computers do not lessen the need for
thinking deeply and that such thinking has its own
rewards.
The question that we ask is
How can we teach students to "teach" the computer?
rather than
How can computer be used to teach physics?
Example of a course that emphasizes simulation:
- Computer simulations provide a opportunity of doing physics
closer to the way research is done.
- Numerical methods more meaningful when part of a simulation
than when taught only as a tool.
- Computer simulations encourage a broader vision of physics
than is usually seen in undergraduate courses. Students can study
models of interest to geologists, biologists, materials
scientists, and social scientists. Course can attract a wide range
of students.
- Simulations provide a way of reaching a deeper understanding
of fundamental physical concepts, particularly by writing
programs with graphics.
- Project oriented, minimum background required.
- Approach close to laboratory experiments.
- Students learn programming skills in context of physics.
- Simulations allow open-ended questions and encourages
creative thinking in contrast to memorization and routine problem
solving.
- The course at Clark involves undergraduate majors in physics, computer science, chemistry, mathematics, biology, and economics and graduate students in physics and chemistry. There is little correlation between the students' background and how well they do in the course. Our experience is that the earlier students take such a course, the better. An excellent example of a first-year course is taught by Wolfgang Christian at Davidson College.
Disadvantages of such a course
- Difficult to add course to curriculum.
- Laboratory course open-ended and time consuming.
- Possibility of neglect of analytical skills (not observed in
practice).
Which Programming Language?
Desirable features
- Platform independent, inexpensive, and easy to
learn.
- Intrinsic graphics statements.
- Libraries for numerical calculations.
- Modular and preferably object-oriented.
- Easy event-based programming capability.
- Useful outside of physics so that language will be
maintained and improved and provide a marketable skill for
students.
- Bit manipulation capability.
- Parallel programming capability or easy route to a language
that does.
Our choice used to be True Basic, but now it is Java.
Also encourage use of Linux in the laboratory to reduce costs and encourage students to learn more about operating systems.
- First in a series of conferences on
how research in physics and research in physics education can be
used to improve the teaching of undergraduate physics.
- The first conference, June 11--15, 2000, will be on
thermal and statistical physics.
- Goal is to bring together workers who are active in research
in thermal and statistical physics, researchers in the new field of
physics education, and people who teach courses in statistical and
thermal physics.
Of course, it is not sufficient to only teach a separate course on computational physics, and we need to also start changing our upper level courses. Statistical and thermal physics is a natural area in which to incorporate integrate computation.
Advertisement: Gould and Tobochnik NSF sponsored
project to enhance the upper division curriculum in thermal and
statistical physics.
Most important goal: Develop a community of teachers and students
to generate course materials and exchange ideas in an open
source environment.
Examples of topics:
- Nature of probability.
- Approach to equilibrium and increase of entropy.
- Microcanonical simulations (molecular dynamics or demon).
Compute subsystem probability to motivate Boltzmann probability.
- Compare different ensembles by doing Monte Carlo simulations
and molecular dynamics. Compare time averages to ensemble averages.
- Quantum Monte Carlo with noninteracting particles to
understand better the significance of indistinguishability.
- Use broad histogram method to improve understanding and
significance of density of states.
- Random walks and diffusion.
- Maximum entropy and image enhancement.
- Traffic flow.
Resources
Please send comments, additions, and corrections to Harvey Gould, hgould@clarku.edu.
Updated 21 April 2000.