Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks
For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.
- Research Organization:
- General Atomics, San Diego, CA (United States); Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Fusion Energy Sciences (FES)
- Contributing Organization:
- ASDEX Upgrade Team
- Grant/Contract Number:
- AC02-09CH11466; FC02-04ER54698
- OSTI ID:
- 2550701
- Journal Information:
- Nuclear Fusion, Journal Name: Nuclear Fusion Journal Issue: 5 Vol. 65; ISSN 0029-5515
- Publisher:
- IOP ScienceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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