obtain a block of time, software developed in-house often translates poorly to evolving chip designs on the massively parallel architectures of today’s national resource machines. In response, a field typically develops its own computational leaders who, purely as a service to their communities, create more robust software and reliable implementations that are tailored to their needs. Those leaders will emerge only if there are appropriate reward mechanisms of career advancement, starting with the support for education and training to learn how to best advance their particular areas of computational science, especially in those science fields where computational readiness is still emerging.

Two models for education and training are being used to advance the computational capabilities of our future workforce: the expansion of computational sciences within existing core disciplines and the development of a distinct undergraduate major or graduate training as a “computational technologist.” The first of these models faces the problem of expanding training and educational opportunities at the interface of the science field with computational and mathematical approaches, and to do this coherently within the time frame typical of a B.S. or Ph.D. degree. It would require integrating computational science topics into existing courses in core disciplines and creating new computational science courses that address current gaps in coverage in degree programs, which in turn call for flexibility in curricula and appropriate faculty incentives. With this approach, the rate at which standardized algorithms and improved software strategies developed by numerical analysts and computer scientists filter into the particular science is likely to be slowed. If education and training exist in undergraduate and graduate programs in a given science and engineering discipline, this is the most common model because it leads to a well-defined career path for the computational scientist within the discipline.

The second model is to develop new academic programs in computational science and engineering that emphasize the concepts involved in starting from a physical problem in science and engineering and developing successful approximations for a physical, mathematical, analytic, discrete, or object model. The student would become robustly trained in linear algebra and partial differential equations, finite difference and finite element methods, particle methods (Monte Carlo and molecular dynamics), and other numerical areas of contemporary interest. A possible limitation is that standardized algorithms and software that are routinely available may need tailoring to suit the needs of the particular science field, which only a field expert can envision. This model is the less common since the reward system for the excellent work of a computational scientist is diluted across multiple scientific/engineering disciplines and because of inherent prejudices in some scientific fields that favor insight over implementation. The primary challenge is to define a career track for a computational generalist who can move smoothly into and out of science domains as the need arises for his or her expertise and have those contributions integrated into a departmental home that recognizes their value.

Astrophysics, atmospheric sciences, and chemical separations are most ready for the first model—direct integration of computational science into the core discipline curriculum—while evolutionary biology has received the attention of statisticians, physicists, and computer scientists to develop something closer to the second model. Both models require expertise in large-scale simulation, such as efficient algorithms, data mining and visualization, parallel computing, and coding and hardware implementation. Ultimately both models will benefit any science field since both are drivers for the cross-disciplinary activity that is to be encouraged for the growing interdisciplinarity of science and engineering.



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