Advanced Computing in the Age of AI|Monday, March 30, 2020
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ARPA-E Brings ML to Power System Design 

The U.S. Energy Department’s research arm is leveraging machine learning technologies to simplify the design process for energy systems ranging from photovoltaics and wind turbines to aircraft engine compressors.

The Advanced Research Projects Agency-Energy, or ARPA-E, last month announced 23 research contracts totaling $15 million to incorporate machine learning into energy product designs. The first-phase contracts are part of an ARPA-E initiative dubbed DIFFERENTIATE, standing for—take a breath—Design Intelligence Fostering Formidable Energy Reduction (and) Enabling Novel Totally Impactful Advanced Technology Enhancements.

David Tew, an ARPA-E program director, said the two-year machine learning effort is focused on the engineering design process with the goal of optimizing power generation systems. Along with wind turbines and photovoltaics, DIFFERENTIATE also will focus on power conversion and heat transfer systems, aerodynamics, photonics and range of foundational energy technologies.

Detailed project descriptions are here.

“Our focus is really on algorithm development,” Tew said in an interview. Among the outcomes will be software packages, some open source, others proprietary. “We want the software to have a commercial impact,” Tew stressed.

Read the full story here at sister web site HPCwire.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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