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New Dataset Tokamak-SI Enhances Interpretation of Tokamak Discharges

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Tokamak-SI, a new dataset designed to interpret tokamak discharges by combining labels from various earlier studies. This dataset aims to create a scalable, time-aligned, and categorical catalog of phenomena such as confinement, detachment, alfven eigenmodes, edge localized modes, sawtooth, tearing modes, and resistive wall modes observed in the DIII-D tokamak. Models trained on Tokamak-SI have demonstrated performance matching or exceeding previous detectors for specific events like alfven eigenmodes, edge localized modes, and tearing modes, facilitating task-specific benchmarking and physics-based archival. AI

IMPACT This dataset and associated models could accelerate research and discovery in plasma physics by enabling more efficient analysis of experimental data.

RANK_REASON The cluster describes a new dataset and associated models for interpreting scientific phenomena, published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Dataset Tokamak-SI Enhances Interpretation of Tokamak Discharges

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The cluster describes a new dataset and associated models for interpreting scientific phenomena, published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 Bahasa(ID) · Nathaniel Chen, Alvin Garcia, Ricardo Shousha, Jalal Butt, Azarakhsh Jalalvand, Egemen Kolemen ·

    Tokamak-SI: Interpreting Tokamak Discharges with Labeled Catalogs

    arXiv:2610.32264v1 Announce Type: cross Abstract: Finding which phenomena occur in a tokamak discharge, and when, is often a manual task. Previous efforts at labeling events have been task-specific, each with its own corpus of data. We introduce Tokamak-SI, which combines the lab…

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