Advances in numerical simulations and data collection are changing the dynamics of scientific research. Technology improvements in computing and sensing are increasing the need for “randomized algorithms” or algorithms that include some form of sampling or randomness in their approach for dealing with massive data, enabling predictive modeling and simulation, and carrying out scientific analysis.
“Preparing for the future means that we must continue to invest in the development of next-generation algorithms for scientific computing,” said Barbara Helland, Associate Director for Advanced Scientific Computing Research, DOE Office of Science. “Foundational research in algorithms is essential for ensuring their efficiency and reliability in meeting the emerging scientific needs of the DOE and the United States.”
Projects selected in today’s announcement cover several topics at the leading-edge of algorithms research. A common theme is to carefully reformulate the computational and data analysis challenges and take full advantage of the underlying structure that is often present within the overall scientific problem. Researchers will explore algorithms for analyzing data from biology, energy storage, and other applications. They will develop fast and efficient algorithms as building blocks for tackling increasingly large data analysis problems from scientific measurements, simulations, and experiments. Projects will also address challenges in solving large-scale computational fluid dynamics and related problems.
The projects are managed by the Office of Advanced Scientific Computing Research within the DOE Office of Science.
The full list of projects and more information can be found here.