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Advanced Computing, Mathematics and Data
Research Highlights

February 2019

Harnessing New Computing Tools to Solve Big Physics Problems

New $10 million collaborative grant to boost deep learning applications

philms

The Department of Energy's Pacific Northwest National Laboratory (PNNL) has been tapped to lead a $10 million, four-year effort to uncover hidden physics using new developments in deep learning, a computing technique that harnesses the power of machine learning and big data.

The Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PhILMs) Center, is a collaboration among PNNL and Sandia National Laboratories, with academic partners at Brown University, Massachusetts Institute of Technology, Stanford University, and the University of California, Santa Barbara.

The PhILMs collaborators are working together to adapt advanced computing techniques to help solve long-standing problems in science and engineering. 

"The most difficult problems in modern physics and engineering involve computing at vastly different scales and across large data sets," said George Karniadakis, the Center Director, who holds a joint appointment at PNNL and Brown University. "By combining the expertise of mathematicians, programmers, and specialists in machine learning techniques, and focusing that expertise on these physics problems, we are aiming to bring new tools to the global physics community."

The research team plans to create, test, and release new open-source computational tools developed through the collaboration and make them freely available through the team's PhILMs web site.

The initial research goals include developing computational frameworks based on deep learning algorithms for applications in combustion, subsurface, and earth systems, as well as machine learning tools for the design of functional materials with tunable properties. Ultimately, the researchers hope to establish scientific machine learning as a new meta-discipline at the intersection of computational mathematics, big data, and deep learning.

PhILMs leadership includes PNNL's Alexandre Tartakovsky, Center Co-Director; Michael L. Parks, Sandia National Laboratories; Mark Ainsworth, Brown University; Eric Darve, Stanford University; and Paul J. Atzberger, University of California, Santa Barbara.

The collaboration is supported by the Applied Mathematics Program within the U.S. Department of Energy, Office of Advanced Scientific Computing Research.


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