“Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments—including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers—is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.”
Read the full Report on Computational Science and Engineering
This is a very interesting report by a US and UE working group on the rise of computational research an engineering in the current digital revolution and presents strategies to leverage the power of CSE in research and education for the next decade.
The section of Conclusions and Recommendations is a must read, here is an excerpt regarding recommendations
- R1: Universities and research institutions should remove disciplinary barriers to allow CSE to realize its broad potential for driving scientific and technological progress in the 21st century. New multidisciplinary research and education structures where CSE is a clearly articulated focus should be increasingly encouraged.
- R2: Funding agencies should develop focused and sustained funding programs that address the specific needs of research in CSE. These programs should acknowledge the multidisciplinary nature of CSE and account for specific research agendas of CSE, including CSE algorithms and software ecosystems as critical instruments of a novel kind of predictive science and access to leading high-performance computing facilities.
- R3: CSE researchers should continue to engage with new application areas and new methodologies in order to realize the full potential of predictive simulation and data analytics as catalysts for innovation. Now ubiquitous large data sets are key to developing new techniques and insights but even greater opportunities are promised by combining them with large-scale simulation models.
R1 and R3 correspond very much to what the Center for Modeling and Simulation in Strasbourg (Cemosis) and AlsaCalcul Services are doing together. The recent evolution of the Master CSMI enables the training of new generations of CSE experts.
R2 correspond to the programs enabled by AMIES and programs such as SIMSEO