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DOE Lab to Explore Machine Learning Tools for Scientific Data Analysis

A team of Oak Ridge National Laboratory researchers has received a three-year, $2 million contract from the Energy Department to study the potential use of machine learning tools in scientific data analysis.

ORNL said Friday it will explore deep learning methods to help scientists understand massive data sets through the Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond project.

“This understanding can help scientists build and support new scientific theories, and help to design better materials,” said Thomas Potok, leader of ORNL’s computational data analytics group.

Potok will carry out the ASCEND project with fellow researchers Robert Patton, Chris Symons, Steven Young and Catherine Schuman.

The teams plans to use the Titan supercomputer at ORNL to test high-performance computing methods and build a deep learning network that will work to process and interpret data from multiple sources such as sensors.

A Battelle-University of Tennessee joint venture manages the laboratory for DOE.

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