Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-955-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-955-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
06 Mar 2020
Model description paper |
| 06 Mar 2020
| 06 Mar 2020
Modelling thermomechanical ice deformation using an implicit pseudo-transient method (FastICE v1.0) based on graphical processing units (GPUs)
Stanford University, Geophysics Department, 397 Panama Mall, Stanford, CA 94305, USA
now at: Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
now at: Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
Aleksandar Licul
Institute of Earth Surface Dynamics, University of Lausanne, 1015 Lausanne, Switzerland
Swiss Geocomputing Centre, University of Lausanne, 1015 Lausanne, Switzerland
Frédéric Herman
Institute of Earth Surface Dynamics, University of Lausanne, 1015 Lausanne, Switzerland
Swiss Geocomputing Centre, University of Lausanne, 1015 Lausanne, Switzerland
Yury Y. Podladchikov
Swiss Geocomputing Centre, University of Lausanne, 1015 Lausanne, Switzerland
Institute of Earth Sciences, University of Lausanne, 1015 Lausanne, Switzerland
Jenny Suckale
Stanford University, Geophysics Department, 397 Panama Mall, Stanford, CA 94305, USA
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Cited
14 citations as recorded by crossref.
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0) E. Wyser et al. 10.5194/gmd-14-7749-2021
- Simulating squirt flow in realistic rock models using graphical processing units (GPUs) Y. Alkhimenkov 10.1093/gji/ggae152
- Digital rock physics: Calculation of effective elastic properties of heterogeneous materials using graphical processing units (GPUs) Y. Alkhimenkov 10.1016/j.cageo.2024.105749
- Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1) A. Sandip et al. 10.5194/gmd-17-899-2024
- Performance portable ice-sheet modeling with MALI J. Watkins et al. 10.1177/10943420231183688
- Ice-flow model emulator based on physics-informed deep learning G. Jouvet & G. Cordonnier 10.1017/jog.2023.73
- A simple computer program for calculating stress and strain rate in 2D viscous inclusion-matrix systems W. Halter et al. 10.1016/j.jsg.2022.104617
- Beyond the Stokes approximation: shallow visco-elastic ice-sheet models J. Bassis & S. Kachuck 10.1017/jog.2023.75
- Resolving Wave Propagation in Anisotropic Poroelastic Media Using Graphical Processing Units (GPUs) Y. Alkhimenkov et al. 10.1029/2020JB021175
- A comparison between three-dimensional, transient, thermomechanically coupled first-order and Stokes ice flow models Z. Yan et al. 10.1017/jog.2022.77
- 101 geodynamic modelling: how to design, interpret, and communicate numerical studies of the solid Earth I. van Zelst et al. 10.5194/se-13-583-2022
- Assessing the robustness and scalability of the accelerated pseudo-transient method L. Räss et al. 10.5194/gmd-15-5757-2022
- Adjoint-based inversion for porosity in shallow reservoirs using pseudo-transient solvers for non-linear hydro-mechanical processes G. Reuber et al. 10.1016/j.jcp.2020.109797
13 citations as recorded by crossref.
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0) E. Wyser et al. 10.5194/gmd-14-7749-2021
- Simulating squirt flow in realistic rock models using graphical processing units (GPUs) Y. Alkhimenkov 10.1093/gji/ggae152
- Digital rock physics: Calculation of effective elastic properties of heterogeneous materials using graphical processing units (GPUs) Y. Alkhimenkov 10.1016/j.cageo.2024.105749
- Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1) A. Sandip et al. 10.5194/gmd-17-899-2024
- Performance portable ice-sheet modeling with MALI J. Watkins et al. 10.1177/10943420231183688
- Ice-flow model emulator based on physics-informed deep learning G. Jouvet & G. Cordonnier 10.1017/jog.2023.73
- A simple computer program for calculating stress and strain rate in 2D viscous inclusion-matrix systems W. Halter et al. 10.1016/j.jsg.2022.104617
- Beyond the Stokes approximation: shallow visco-elastic ice-sheet models J. Bassis & S. Kachuck 10.1017/jog.2023.75
- Resolving Wave Propagation in Anisotropic Poroelastic Media Using Graphical Processing Units (GPUs) Y. Alkhimenkov et al. 10.1029/2020JB021175
- A comparison between three-dimensional, transient, thermomechanically coupled first-order and Stokes ice flow models Z. Yan et al. 10.1017/jog.2022.77
- 101 geodynamic modelling: how to design, interpret, and communicate numerical studies of the solid Earth I. van Zelst et al. 10.5194/se-13-583-2022
- Assessing the robustness and scalability of the accelerated pseudo-transient method L. Räss et al. 10.5194/gmd-15-5757-2022
Latest update: 13 Dec 2024
Short summary
Accurate predictions of future sea level rise require numerical models that predict rapidly deforming ice. Localised ice deformation can be captured numerically only with high temporal and spatial resolution. This paper’s goal is to propose a parallel FastICE solver for modelling ice deformation. Our model is particularly useful for improving our process-based understanding of localised ice deformation. Our solver reaches a parallel efficiency of 99 % on GPU-based supercomputers.
Accurate predictions of future sea level rise require numerical models that predict rapidly...
