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ORNL researchers turn to ‘deep learning’ to solve science’s big data problem

Posted at 2:56 pm September 3, 2017
By Oak Ridge Today Staff Leave a Comment

Scientists will use Oak Ridge National Laboratory’s computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. (Photo credit: Jason Richards/Oak Ridge National Laboratory, U.S. Department of Energy)

Scientists will use Oak Ridge National Laboratory’s computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. (Photo credit: Jason Richards/Oak Ridge National Laboratory, U.S. Department of Energy)

 

By Scott Jones, 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.

The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world’s most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL’s Thomas Potok, includes Robert Patton, Chris Symons, Steven Young, and Catherine Schuman.

While deep learning has long been used to classify relatively simple data such as photographs, today’s scientific data presents a much greater challenge because of its size and complexity. Deep learning offers the potential to truly change the way in which researchers use massive datasets to solve challenges spanning the scientific spectrum.

For example, neutron scattering data collected at ORNL’s Spallation Neutron Source contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties.

“This understanding can help scientists build and support new scientific theories, and help to design better materials,” Potok said. [Read more…]

Filed Under: Front Page News, Oak Ridge National Laboratory, Top Stories, U.S. Department of Energy Tagged With: A study of complex deep learning networks on high performance neuromorphic and quantum computers, Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond, artificial neural networks, ASCEND, Catherine Schuman, Chris Symons, deep learning, machine learning, massive datasets, Oak Ridge Leadership Computing Facility, Oak Ridge National Laboratory, Office of Science, ORNL, Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, Robert Patton, Scott Jones, self-learning devices, Spallation Neutron Source, Steven Young, Thomas Potok, Titan supercomputer

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