5 citations · 7 across the 13 of their papers we have counts for
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COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
Zesheng Liu, Maryam Rahnemoonfar
In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT e…
From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting
Zesheng Liu, Maryam Rahnemoonfar
Accurate long-range prediction of geophysical systems is difficult due to strongly nonlinear dynamics, the high computational cost of full-physics simulations, and the error accumu…
Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces
Zesheng Liu, Maryam Rahnemoonfar
Internal ice layers imaged by radar provide key evidence of snow accumulation and ice dynamics, but radar-derived layer boundary observations are often incomplete, with discontinuo…
PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation
Zesheng Liu, Doyup Kwon, Ning Lin +1
Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for large scenario ensembles and…
K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data
Zesheng Liu, Maryam Rahnemoonfar
Subsurface stratigraphy contains important spatio-temporal information about accumulation, deformation, and layer formation in polar ice sheets. In particular, variations in intern…
GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction
Zesheng Liu, Maryam Rahnemoonfar
Graph transformers have demonstrated remarkable capability on complex spatio-temporal tasks, yet their depth is often limited by oversmoothing and weak long-range dependency modeli…