D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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2 Pith papers cite this work, alongside 873 external citations. Polarity classification is still indexing.
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Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.
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D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
Adding MAR climate-model features plus a multi-branch GraphSAGE/temporal-convolution design with adaptive fusion reduces deep-ice-layer thickness RMSE by 21.01% over a no-knowledge multi-branch baseline on the SRED Greenland dataset.