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Ornl Researchers Turn to Deep Learning to Solve Science's Big Data Problem


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The Titan supercomputer at Oak Ridge National Laboratory.

A team of researchers from Oak Ridge National Laboratory has been awarded nearly $2 million over three years from the U.S. Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis.

Credit: Jason Richards/Oak Ridge National Laboratory, U.S. Dept. of Energy

Researchers at Oak Ridge National Laboratory (ORNL) have received a three-year, $2-million grant from the U.S. Department of Energy (DoE) to study the potential of machine learning in revolutionizing scientific data analysis.

The DoE grant will fund the Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond project, whose goal is to use deep learning to help researchers understand massive datasets produced at the world's most sophisticated scientific facilities.

The ORNL researchers are seeking to revolutionize current analysis paradigms by using deep learning to identify patterns in scientific data and alert scientists to potential new discoveries.

The ORNL team plans to develop a deep-learning network that can decipher data from hundreds of thousands of inputs.

"We revealed new capabilities not feasible with conventional computing architectures," says ORNL's Thomas Potok. "It potentially allows us to solve very complicated problems unsolvable with current computing technologies."

From Oak Ridge National Laboratory
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