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From the Editors: The Way Forward—Learning from Our Past


Pages: p. 2

Testimony from past readers drives this issue's content and approach. High on their wish list are a greater emphasis on science content, a more tutorial approach to computation, and a stronger, more utilitarian character. These are appropriate because science sets the context for—and computation, the practice of—computing in science and engineering. But isn't this what we are doing? Our charter commits us to build communities of practice that cross disciplines within the sciences, engineering, mathematics, and computation. Are we perhaps honoring this commitment, more often than not, in its breach?

This issue offers three responses. First, its theme is multiphysics modeling. Having been an experimental physicist for most of my career, modeling naturally brings to mind the fitting of curves to measurements and testing statistical agreement of experiment with theory. Sure, I read the journals and am aware that detailed modeling of complex physical systems has become a big business with the advent of relatively cheap and fast computing. Still, at the recent SIAM meetings last July, I was unprepared for the impact of learning about this methodology—a neat blend of computation informed by science. I promptly collared the symposium presenters, revealing my whetted appetite and wish to learn more. The result is this theme issue of CiSE.

Second, the Education department has been "put on hold" for one issue due to a "plague of riches." There was one more theme article than anticipated and the Technology Reviews department contribution turned out much larger than could be accommodated in a single issue. Instead, the Education department's authors present a short segue into the anticipated continuation of their serial tutorial review of discrete Fourier transforms in the next issue. DFT's and FFT's ... now there's something I thought I knew a lot about. But as often happens, the sharpness that lies within a comprehensive view is dulled by narrow, ritual practice. Gradually, we learn to follow a best practice, forgetting what underlies it. Judging by the number of download requests we received for part one of this article (it currently tops our list), other readers may seem to feel the same way—or at least have a considerable interest in this sort of information.

Finally, the Technology Reviews department, after an earlier qualitative and introductory article, features the first in a series of heavy-duty reviews of three major scientific/engineering productivity packages—Maple, Mathematica, and Matlab. This one focuses on their use for educational applications. The structure and content for these experimental reviews comes from a wish that our departmental articles have utility, but we aren't leaving the results to chance. We've also commissioned a survey to accompany this article; the intention is a "usability analysis" of if and how readers have actually used this article in their work.

We're seeking the way forward and are counting on your help. Please keep those cards and letters coming!

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