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A Learning By Doing Approach To Teaching Computational Physics

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2010 Annual Conference & Exposition


Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010



Conference Session

Innovations in Teaching Physics or Engineering Physics

Tagged Division

Engineering Physics & Physics

Page Count


Page Numbers

15.46.1 - 15.46.11

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Paper Authors

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Radian Belu Drexel University

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Alexandru Belu Case Western Research University

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

A Learning-by-Doing Approach to Teaching Computational Physics


Scientific research is becoming unthinkable without computing. The ubiquity of computerized instrumentation and detailed simulations generates scientific data in volumes that no longer can be understood without computation. Computational physics is a rapidly growing subfield of physics and computational science in large part because computers can solve previously intractable problems or simulate natural processes that do not have analytic solutions. One can easily argue that all graduates of science or engineering programs should have the opportunity to develop good computing skills by the time they complete their studies. However, the depth and range of skills needed varies considerably – even in a single discipline such as physics. Moreover, the interests, backgrounds, and abilities of students taking physics courses vary widely, whereas the number of instructors with scientific computing skills has been rather limited. Providing appropriate courses and instruction in computational physics for such diverse student population is a challenge. On the other hand, computational physics provides exciting teaching opportunities that can complement traditional methods of teaching in the lecture or the laboratory. We use a laboratory project-based approach, where the students are learning by doing. The course is divided into two sections, lecture and laboratory session. During the laboratory session, the students work at mid-term and final projects, while the lecture the programming, numerical and computational techniques and methods are discussed. The usefulness of this approach is evaluated by surveys conducted every semester, and feedback from other educators is highly appreciated.

I. Introduction

Computational physics is an independent way of doing physics, and an essential tool of the physics research. Numerical computations are essential to further understanding of physics problems, and computers and computing play a central role in much of modern scientific research. Almost all analytical theories require the help of a computer to complete the calculations. On the experimental side, computers are essential for the control of experiments and the collection and analysis of data. However, computational physics also includes a fundamentally different way of doing physics that goes beyond using the computer as a specific tool. We have in mind the part of computational physics, called computer simulations1, 8-11, in contrast to many of the tasks listed above which we classify as numerical analysis. Using a computer to model physical systems is at its best more art than science. The successful computation al physicist exploits the numerical power of the computer through a mix of numerical analysis, analytical models, and programming to solve otherwise intractable problems. It is a skill that can be acquired and refined - knowing how to set up the simulation, what numerical methods to employ, how to implement them efficiently, when to trust the accuracy of the results. In the last two decades, however, computational physics has largely been neglected in the standard university physics curriculum1-5. In part, this is because it requires balanced integration of three commonly disjoint disciplines: physics, numerical analysis, and computer programming (Figure 1). The lack of computing hardware suitable for teaching situations

Belu, R., & Belu, A. (2010, June), A Learning By Doing Approach To Teaching Computational Physics Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky.

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