GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

Author:

Yao SiyuanORCID,Han Jun,Wang Chaoli

Funder

National Science Foundation

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Human-Computer Interaction,General Engineering

Reference47 articles.

1. DL4SciVis: A state-of-the-art survey on deep learning for scientific visualization;Wang;IEEE Trans Vis Comput Graphics,2022

2. V2V: A deep learning approach to variable-to-variable selection and translation for multivariate time-varying data;Han;IEEE Trans Vis Comput Graphics,2021

3. Deep learning-based parameter transfer in meteorological data;Farokhmanesh;Artif Intell Earth Syst,2023

4. Learning meaningful controls for fluids;Chu;ACM Trans Graph,2021

5. Gu P, Han J, Chen DZ, Wang C. Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning. In: Proceedings of IEEE pacific visualization symposium. 2022, p. 31–40.

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