New research funding
Energy Department Funds Three Projects on Data, Computational Infrastructure Research
Three collaborative projects led by five universities will receive a combined $15.1 million in funding from the Department of Energy.
According to the DOE, the efforts are aimed at advancing the development of a “flexible, multi-tiered” data and computational infrastructure to support a collection of on-demand scientific data processing tasks and computationally intensive simulations.
The selected organizations will use the funding to accelerate research in the fields of environmental and materials science, as well as enhance simulation capabilities, the DOE said Thursday.
One of the projects will be carried out by scientists from the University of Texas-Austin, the University of Notre Dame, Louisiana State University and Lawrence Berkeley National Laboratory. The project seeks to address mitigation strategies for gulf coastal flooding events using artificial intelligence and machine learning techniques.
In another initiative, Berkeley Lab is also collaborating with the University of Connecticut to design, manufacture and test new materials for property applications in batteries, sensors and energy storage.
The third project that will receive funding is Berkeley Lab’s collaboration with Argonne National Laboratory and the University of Southern California. The effort involves the development of AI/ML-based methods to simulate and verify the performance of large, distributed computing infrastructure.
Collaborations between scientific disciplines will pave the way for the future of scientific discovery by using a diverse set of knowledge, skills and tools in new ways to tackle critical problems, said Barbara Helland, the DOE’s associate director of science for advanced scientific computing research.
The selected projects are expected to revolutionize the scientific productivity of U.S. facilities while solving some of the country’s pressing problems, Helland noted.
Category: Federal Civilian
Tags: AI Argonne National Laboratory artificial intelligence Barbara Helland computational infrastructure computing data Department of Energy DoE energy storage Energy.gov federal civilian funding Lawrence Berkeley National Laboratory Louisiana State University machine learning ML research The University of Texas at Austin University of Connecticut University of Notre Dame University of Southern California