Multiscale simulation of plasma flows using active learning.
Phys Rev E
; 102(2-1): 023310, 2020 Aug.
Article
in En
| MEDLINE
| ID: mdl-32942385
ABSTRACT
Plasma flows encountered in high-energy-density experiments display features that differ from those of equilibrium systems. Nonequilibrium approaches such as kinetic theory (KT) capture many, if not all, of these phenomena. However, KT requires closure information, which can be computed from microscale simulations and communicated to KT. We present a concurrent heterogeneous multiscale approach that couples molecular dynamics (MD) with KT in the limit of near-equilibrium flows. To reduce the cost of gathering information from MD, we use active learning to train neural networks on MD data obtained by randomly sampling a small subset of the parameter space. We apply this method to a plasma interfacial mixing problem relevant to warm dense matter, showing considerable computational gains when compared with the full kinetic-MD approach. We find that our approach enables the probing of Coulomb coupling physics across a broad range of temperatures and densities that are inaccessible with current theoretical models.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Phys Rev E
Year:
2020
Document type:
Article
Affiliation country:
United States