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HPC & AI Wall StreetWASHINGTON, Aug. 25, 2023 — The U.S. Department of Energy (DOE) announced $16 million in funding for four projects in scientific machine learning for the predictive modeling and simulation of complex systems.
High-performance computational models and simulations, combined with data from experiments and observations, are being used to increase our scientific understanding of composite materials, climate, turbulent fluid flow, and other complex systems and processes.
“Basic research for scientific computing and machine learning continues to have a wide impact across a range of applications,” said Ceren Susut, DOE Acting Associate Director of Science for Advanced Scientific Computing Research. “These projects are important for advancing our modeling, simulation, and data analysis capabilities for scientific discovery and innovation.”
Projects include:
- A collaboration led by Pacific Northwest National Laboratory, partnering with Spelman College, for quantifying uncertainties and improving predictions in atmospheric simulations and measurements.
- A project led by Johns Hopkins University for research on the properties and behavior of additively manufactured composites in materials science and turbulent, high-speed fluid flow in aerospace engineering applications.
The projects were selected by competitive peer review under the DOE Funding Opportunity Announcement for Scientific Machine Learning for Complex Systems, DE-FOA-0002958.
Total funding is $16 million for projects lasting up to four years in duration, with $3.3 million in Fiscal Year 2023 dollars and outyear funding contingent on congressional appropriations. The list of projects and more information can be found on the Advanced Scientific Computing Research program homepage.
Selection for award negotiations is not a commitment by DOE to issue an award or provide funding. Before funding is issued, DOE and the applicants will undergo a negotiation process, and DOE may cancel negotiations and rescind the selection for any reason during that time.
source: DOE Office of Science
