Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session W16: Machine Learning and Data in Polymer Physics II
3:00 PM–6:00 PM,
Thursday, March 17, 2022
Room: McCormick Place W-184A
Sponsoring Units: DPOLY DBIO DCOMP GDS
Chair: Debra Audus, NIST
Abstract: W16.00011 : Application of machine-learned constitutive relations for well-entangled polymer melt flows*
5:24 PM–5:36 PM
John J Molina
Compared to currently used hierarchical simulation methods, which directly couple the microscopic polymer dynamics and the macroscopic flows, our method is ~100 times faster and drastically reduces the statistical fluctuations in the flow predictions, while maintaining a clear connection to the underlying molecular model. In this presentation, we will use benchmark Multi-Scale simulation results to present a detailed error analysis of our ML predictions and their computational efficiency.
This work was partially supported by Japan Society for the Promotion of Science Kakenhi (19H01862, 20K037865), SPIRITS 2020 of Kyoto University, Ogasawara Foundation.
*This work was partially supported by Japan Society for the Promotion of Science Kakenhi (19H01862, 20K037865), SPIRITS 2020 of Kyoto University, Ogasawara Foundation.
The American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics.
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