Mimicking complex dislocation dynamics by interaction networks
Two-dimensional discrete dislocation models exhibit complex dynamics in relaxation and under external loading. This is manifested both in the time-dependent velocities of individual dislocations and in the ensemble response, the strain rate. Here we study how well this complexity may be reproduced using so-called Interaction Networks, an artificial intelligence method for learning the dynamics of complex interacting systems. We test how to learn such networks using creep data, and show results on reproducing individual and collective dislocation velocities. The quality of reproducing the interaction kernel is discussed.
- 5.S. Wiewel, M. Becher, N. Thuerey, https://doi.org/arXiv:1802.10123 (2018)
- 12.P. Battaglia, R. Pascanu, M. Lai, D.J. Rezende, in Advances in Neural Information Processing Systems (NIPS, 2016), Vol. 29, p. 4502 Google Scholar
- 19.J.P. Hirth, J. Lothe, Theory of dislocations (Krieger, Malabar, FL, 1982) Google Scholar