Since the advent of the computer, its potential to advance scientific investigation has been recognized. From the design and application of early computers to aid in the cracking of codes during WWII and the simulations leading to the hydrogen bomb in 1952, the use of computers and the development of algorithms and systems in the service of science, “computational science,” was born. Since that time, the computer has become an indispensable appliance of business and an essential facet of science. From the statistical analysis of the mutations of HIV to the simulations describing the evolution of the universe or the flow of traffic on the freeway; from information extracted in a meaningful way from large databases to the transfer of money when an ATM card is used, the computer, sophisticated algorithms, large networks and databases are central to our life – and in science, essential. In science, as problems become more complicated and datasets larger, the techniques needed to design the experiments and make sense of it all requires both the computer and algorithms for the development of the necessary systems, simulations, and analysis. The activity which enables this accomplishment – using the computer as a research tool – is the field of computational science. It clearly involves mathematics, applied mathematics, computer science, and statistics, along with an understanding of the science of the applications. This activity involves original thought and discovery, the ability to design and use tools and systems, and a facility for communicating with those from other fields.
The term computational science is a broad one that describes scientific and engineering inquiry in which the computer plays an essential role. It involves the development of models, systems, algorithms, and simulations in order to solve concrete problems. For instance, in the NSF Program Description for Computational Mathematics, this program “supports mathematical research in areas of science where computing plays a central and essential role, emphasizing algorithms, numerical methods, and symbolic methods. The prominence of computation in the research is a hallmark of the program. Proposals ranging from single-investigator projects that develop and analyze innovative computational methods to interdisciplinary team projects that not only create new mathematical and computational techniques but use them to model, study, and solve important application problems are encouraged.” The primary emphasis in any of the computational sciences is the solution to a problem and development of useful computational tools rather than the proof of a theorem.
The need for these programs, as part of STEM (Science, Technology, Engineering and Mathematics) education has been recognized by several influential groups. The most powerful statement is the report from the President’s Information Technology Advisory Committee (PITAC) in 2005 entitled “Computational Science: Ensuring America’s Competitiveness.” In this report, the Principal Finding was:
Computational science is now indispensable to the solution of complex problems in every sector, from traditional science and engineering domains to such key areas as national security, public health, and economic innovation. Advances in computing and connectivity make it possible to develop computational models and capture and analyze unprecedented amounts of experimental and observational data to address problems previously deemed intractable or beyond imagination. Yet, despite the great opportunities and needs, universities and the Federal government have not effectively recognized the strategic significance of computational science in either their organizational structures or their research and educational planning. These inadequacies compromise U.S. scientific leadership, economic competitiveness, and national security.
The principal recommendation of special interest to this proposal, is:
Universities and the Federal government’s R&D agencies must make coordinated, fundamental, structural changes that affirm the integral role of computational science in addressing the 21st century’s most important problems, which are predominantly multidisciplinary, multi-agency, multi sector, and collaborative. To initiate the required transformation, the Federal government, in partnership with academia and industry, must also create and execute a multi-decade roadmap directing coordinated advances in computational science and its applications in science and engineering disciplines.
Traditional disciplinary boundaries within academia and Federal R&D agencies severely inhibit the development of effective research and education in computational science. The paucity of incentives for longer-term multidisciplinary, multi-agency, or multi-sector efforts stifles structural innovation. To confront these issues, universities must significantly change their organizational structures to promote and reward collaborative research that invigorates and advances multidisciplinary science. They must also implement new multidisciplinary structures and organizations that provide rigorous, multifaceted educational preparation for the growing ranks of computational scientists the Nation will need to remain at the forefront of scientific discovery.
The Society for Industrial and Applied Mathematics (SIAM) asked a distinguished group to study recent developments in CSE (Computational Science and Engineering) education and to give recommendations for SIAM’s role in this important effort. The report was published in 2007. It surveyed graduate programs in computational science, M.S. and Ph.D., in the U.S. and worldwide (list in Appendix 2). Besides examining several programs in detail, they state:
One point we would like to emphasize in this document is that CSE is a legitimate and important academic enterprise, even if it has yet to be formally recognized as such at some institutions. Although it includes elements from computer science, applied mathematics, engineering and science, CSE focuses on the integration of knowledge and methodologies from all of these disciplines, and as such is a subject, which is distinct from any of them.
The study group recognized that common to all successful programs was a foundation in mathematics, applied mathematics, statistics and computer science and an application area involving an interdisciplinary team.