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DOE to Spend $15.1M for Computational, Data Infrastructure for Science Research


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The U.S. Department of Energy announced $15.1 million for three collaborative research projects at five universities to advance the development of a flexible multi-tiered data and computational infrastructure to support a diverse collection of on-demand scientific data processing tasks and computationally intensive simulations.

Scientists from The University of Texas–Austin, the University of Notre Dame, Louisiana State University, and Lawrence Berkeley National Laboratory will address mitigation strategies for gulf coastal flooding events due to extreme weather with artificial intelligence and machine learning techniques that combine experimental data with computer simulations.

Scientists from the University of Connecticut and Lawrence Berkeley National Laboratory will couple experimental data with simulations using AI/ML techniques to design, manufacture, and test new materials with uniquely designed properties for potential applications in batteries, sensors, and energy storage.

Scientists from the University of Southern California, Argonne National Laboratory, and Lawrence Berkeley National Laboratory will develop AI/ML-based methods to simulate and experimentally verify the performance of large, distributed computing infrastructures.

From U.S. DOE Office of Science
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