Transfer learning nonlinear plasma dynamic transitions in low dimensional embeddings via deep neural networks
Journal Article
·
· Machine Learning: Science and Technology
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- University of California, Irvine, CA (United States)
- Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States); Princeton University, Princeton, NJ (United States)
Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel, data-driven model reduction methods, coupled with detection of abnormal modes with plasma physics, opens a unique opportunity to identify plasma instabilities through automated construction of parsimonious models that can be tuned to balance accuracy and cost. Our fusion transfer learning (FTL) model demonstrates success in rapidly reconstructing nonlinear kink mode structures by learning from a limited amount of nonlinear simulation data. The knowledge transfer process leverages a pre-trained neural encoder–decoder network, initially trained on linear simulations, to effectively capture nonlinear dynamics. The low-dimensional embeddings extract the coherent structures of interest, while preserving the inherent dynamics of the complex system. Experimental results highlight FTL’s capacity to capture transitional behaviors and dynamical features in plasma dynamics—a task often challenging for conventional methods. The model developed in this study is generalizable and can be extended broadly through transfer learning to address various magnetohydrodynamics modes.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
- Sponsoring Organization:
- USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- Grant/Contract Number:
- AC02-05CH11231; AC02-09CH11466
- OSTI ID:
- 2557550
- Alternate ID(s):
- OSTI ID: 2551903
OSTI ID: 2567023
OSTI ID: 2564375
- Journal Information:
- Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 2 Vol. 6; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English