Normally, when testing the behavior of materials under high heat or explosive conditions, researchers have to run simulation after simulation, a data-intensive process that can take days even on a supercomputer. However, with a deep learning algorithm created by Stephen Baek, Phong Nguyen and their research team, the process takes less than a second on a laptop.
Baek and Nguyen’s latest findings, part of research sponsored by the Air Force Office of Scientific Research and the Designing Materials to Revolutionize and Engineer our Future program within the National Science Foundation, were published in the most recent volume of the Science Advances journal. Collaborators include mechanical engineering professor H.S. Udaykumar and a team of computational mechanics at the University of Iowa.
Baek and Nguyen, both faculty members in the UVA School of Data Science, believe the algorithm they developed, called physics-aware recurrent convolutions, or PARC, has profound implications in materials science and in other areas governed by complex physical processes, ranging from climate change models to the growth dynamics of some cancers and other diseases to atmospheric conditions on Mars. Read More.
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