Data for reproducing the figures of the paper Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

; ; ; ; ; ; ; ;

Issue date: 2025

Rights:
Creative Commons Attribution 4.0 International (CC BY)

Cite as:

Jalalvand, Azarakhsh, Kim, SangKyeun, Seo, Jaemin, Hu, Qiming, Curie, Max, Steiner, Peter, Nelson, Andrew, Na, Yong-Su, & Kolemen, Egemen. (2025). Data for reproducing the figures of the paper Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas [Data set]. Version 1. Princeton University. https://doi.org/10.34770/nex7-3y26

@electronic{jalalvand_azarakhsh_2025,
  author      = {Jalalvand, Azarakhsh and
                Kim, SangKyeun and
                Seo, Jaemin and
                Hu, Qiming and
                Curie, Max and
                Steiner, Peter and
                Nelson, Andrew and
                Na, Yong-Su and
                Kolemen, Egemen},
  title       = {{Data for reproducing the figures of the
                paper Multimodal Super-Resolution: Disco
                vering hidden physics and its applicatio
                n to fusion plasmas}},
  version     = 1,
  publisher   = {{Princeton University}},
  year        = 2025,
  url         = {https://doi.org/10.34770/nex7-3y26}
}

: Description:

This deposit contains the raw data for reproducing research results of the paper Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas. The main contribution of this work is to utilize machine learning techniques to reconstruct and enhance the resolution of a diagnostic measurement from other available diagnostics in a system. The proposed techniques is called Diag2Diag.
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Authors	Jalalvand, Azarakhsh (Princeton University) Kim, SangKyeun (Princeton Plasma Physics Laboratory) Seo, Jaemin (Chung-Ang University) Hu, Qiming (Princeton Plasma Physics Laboratory) Curie, Max (Princeton University) Steiner, Peter (Princeton University) Nelson, Andrew (Columbia University) Na, Yong-Su (Seoul National University) Kolemen, Egemen (Princeton University)
Domains	Engineering
Communities	Princeton Plasma Physics Laboratory
Subcommunities	DIII-D
Issue date	2025
Date Created	2025-02-26
Date Modified	2025-03-17
Version	1
URI	https://doi.org/10.34770/nex7-3y26
Publisher	Princeton University
Type	Dataset
Funders	United States Department of Energy, DE-FC02- 04ER54698 United States Department of Energy, DE-SC0024527 United States Department of Energy, DE-SC0015480 United States Department of Energy, DE-SC0022270 United States Department of Energy, DE-SC0022272 NRF, RS-2024-00346024 Princeton Laboratory for Artificial Intelligence, 2025-97
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