Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Journal of Glaciology vol. 71 (2025) |
|---|---|
| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | |
| Έκδοση: |
Cambridge University Press
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| Θέματα: | |
| Διαθέσιμο Online: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 0022-1430 | ||
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| 024 | 7 | |a 10.1017/jog.2024.93 |2 doi | |
| 035 | |a 3154589723 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Koo, Younghyun |u Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA; Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USA | |
| 245 | 1 | |a Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling | |
| 260 | |b Cambridge University Press |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The Ice-sheet and Sea-level System Model (ISSM) provides numerical solutions for ice sheet dynamics using finite element and fine mesh adaption. However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. Since they are not appropriate for the irregular meshes of ISSM, we use a graph convolutional network (GCN) to replicate the adapted mesh structures of the ISSM. When applied to transient simulations of the Pine Island Glacier (PIG), Antarctica, the GCN successfully reproduces ice thickness and velocity with a correlation coefficient of approximately 0.997, outperforming non-graph models, including fully convolutional network (FCN) and multi-layer perceptron (MLP). Compared to the fixed-resolution approach of the FCN, the flexible-resolution structure of the GCN accurately captures detailed ice dynamics in fast-ice regions. By leveraging 60–100 times faster computational time of the GPU-based GCN emulator, we efficiently examine the impacts of basal melting rates on the ice sheet dynamics in the PIG. | |
| 651 | 4 | |a Pine Island Glacier | |
| 651 | 4 | |a Greenland | |
| 651 | 4 | |a Antarctica | |
| 653 | |a Glaciers | ||
| 653 | |a Ice thickness | ||
| 653 | |a Central processing units--CPUs | ||
| 653 | |a Deep learning | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Ice cover | ||
| 653 | |a Multilayers | ||
| 653 | |a Sea level | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Multilayer perceptrons | ||
| 653 | |a Ice sheet dynamics | ||
| 653 | |a Neural networks | ||
| 653 | |a Approximation | ||
| 653 | |a Computer applications | ||
| 653 | |a Machine learning | ||
| 653 | |a Correlation coefficient | ||
| 653 | |a Sheet modelling | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Statistical models | ||
| 653 | |a Glaciation | ||
| 653 | |a Velocity | ||
| 653 | |a Modelling | ||
| 653 | |a Partial differential equations | ||
| 653 | |a Ice | ||
| 653 | |a Emulators | ||
| 653 | |a Graphs | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Ice sheets | ||
| 653 | |a Dynamics | ||
| 653 | |a Information processing | ||
| 653 | |a Ice environments | ||
| 653 | |a Computing time | ||
| 653 | |a Shear stress | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Rahnemoonfar, Maryam |u Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA; Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USA | |
| 773 | 0 | |t Journal of Glaciology |g vol. 71 (2025) | |
| 786 | 0 | |d ProQuest |t Research Library | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3154589723/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3154589723/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3154589723/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |