Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session M71: Poster Session III (11:15am - 2:15pm)
Wednesday, March 4, 2020
Room: Exhibit Hall C/D
Abstract: M71.00210 : Predicting the Steady Flow of a Fluid with Particles by Deep Learning*
View Presentation Abstract
Using deep learning, the authors predicted the steady flow around a large number of particles and examined the effectiveness of the prediction for the study of materials development. The particle-fluid interaction is computed using the Smoothed Profile Method . After learning the flow that passes the particles obtained by iterative calculation, the deep learning quickly and accurately predicted the flow of the system with unknown particle concentration and arrangement. The fluid force applied to each particle was also accurately predicted.
 X. Guo, W. Li, and F. Iorio, KDD ’16 (2016).
 O. Hennigh, arXiv, arXiv:1710.10352 (2017).
 Y. Nakayama and R. Yamamoto, Phys. Rev. E, 71, 036707 (2005).
*This work was funded by New Energy and Industrial Technology Development Organization of Japan (NEDO) Grant (P16010).
The American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics.
1 Physics Ellipse, College Park, MD 20740-3844
Editorial Office 1 Research Road, Ridge, NY 11961-2701 (631) 591-4000
Office of Public Affairs 529 14th St NW, Suite 1050, Washington, D.C. 20045-2001 (202) 662-8700