EarthGAN: Can we visualize the Earth's mantle convection using a surrogate model?
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Abstract (arXiv)
Scientific simulations are often used to gain insight into foundational questions. However, many potentially useful simulation results are difficult to visualize without powerful computers. In this research, we seek to build a surrogate model, using a generative adversarial network, to allow for the visualization of the Earth's Mantle Convection data set on readily accessible hardware. We present our preliminary method and results, and all code is made publicly available. The preliminary results show that a surrogate model of the Earth's Mantle Convection data set can generate useful results. A comparison to the "ground-truth" is provided.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 25 Oct 2021.
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References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
- 2017Improved Training of Wasserstein GANscited by 2,223
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2020AI-assisted super-resolution cosmological simulationscited by 15
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