Microtearding Mode Study in NSTX Using Machine Learning Enhanced Reduced Model

Max T. Curie, Joel Larakers,Jason Parisi,Gary Staebler,Stefano Munaretto,Walter Guttenfelder,Emily Belli,David R. Hatch,Mate Lampert,Galina Avdeeva,Tom Neiser,Sterling Smith,Ahmed Diallo,Oak Nelson,Stanley Kaye,Eric Fredrickson,Joshua M Manela, Shelly Lei,Michael Halfmoon, Matthew M Tennery,Ehab Hassan

arXiv (Cornell University)（2023）

Cited 1|Views96

Abstract

This article presents a survey of NSTX cases to study the microtearing mode (MTM) stabilities using the newly developed global reduced model for Slab-Like Microtearing modes (SLiM). A trained neutral network version of SLiM enables rapid assessment (0.05s/mode) of MTM with 98% accuracy providing an opportunity for systemic equilibrium reconstructions based on the matching of experimentally observed frequency bands and SLiM prediction across a wide range of parameters. Such a method finds some success in the NSTX discharges, the frequency observed in the experiment matches with what SLiM predicted. Based on the experience with SLiM analysis, a workflow to estimate the potential MTM frequency for a quick assessment based on experimental observation has been established.

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System-Level ESD Test

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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