Credits
Main developpers
| Name | Contributions |
|---|---|
| Guillaume Jouvet, UNIL | Primary source code author of most of modules and their documentation, design of the PI-CNN in forward [1,3] and inverse modes [2]. |
| Brandon Finley, UNIL | Core code and software design, hydra integration, code profiling, docker, documentation framework design, texture module, TBC |
Contributors
(get in touch if you notice any missing contribution)
| Name | Contributions |
|---|---|
| Flavio Calvo | Support from the release IGM1 to IGM2 |
| Samuel Cook | Implementation of functions specific for global modelling in the data_assimilation module, inclusion of RGI7 in oggm_shop |
| Guillaume Cordonnier | Co-design of the PI-CNN in forward mode, original implementation of the thk module |
| Alex Jarosch | Bueler2005C's benchmark case in the example gallery |
| Andreas Henz | Loading icemasks from shape files in input modules |
| Tancrède Leger | Climate module XXXX |
| Fabien Maussion | Original oggm_shop module, and support for the integration of OGGM-based clim_oggm and smb_oggm modules, and instructed OGGM routine. |
| Jürgen Mey | avalanche and glex modules |
| Oskar Hermann | Vizualization tool anim_plotly |
| Dirk Scherler | Support for the verication of the particle module |
| Margot Sirdey | Support from the release IGM1 to IGM2, PiPy setup |
| Gillian Smith | Bug fixes |
| Patrick Schmitt | Vizualization tool, TBC |
| Claire-Mathile Stücki | Update of vert_flow and particle modules |
| Ethan Welthy | GlaThiDa file reading |
Citing IGM
The foundational concepts of IGM are as follows: The modeling approach using data-driven ice flow convolutional neural networks (CNN) was introduced in [3], the inversion method was introduced in [2], and the physics-informed ice flow surrogate neural network (SNN) was introduced in [13]. There is currently an in-progress IGM technical paper that provides an overview of the physical components, modules, and capabilities of IGM. Until the technical paper is finalized, [1] serves as the most up-to-date reference for understanding IGM concepts, and should therefore be used for referencing IGM.
[1] Jouvet, G., & Cordonnier, G. (2023). Ice-flow model emulator based on physics-informed deep learning. Journal of Glaciology, 69(278), 1941-1955.
[2] Jouvet, G. (2023). Inversion of a Stokes glacier flow model emulated by deep learning. Journal of Glaciology, 69(273), 13-26.
[3] Jouvet, G., Cordonnier, G., Kim, B., Lüthi, M., Vieli, A., & Aschwanden, A. (2022). Deep learning speeds up ice flow modelling by several orders of magnitude. Journal of Glaciology, 68(270), 651-664.
Bibtex entries
@article{IGM,
author = "Jouvet, Guillaume and Cordonnier, Guillaume and Kim, Byungsoo and Lüthi, Martin and Vieli, Andreas and Aschwanden, Andy",
title = "Deep learning speeds up ice flow modelling by several orders of magnitude",
DOI = "10.1017/jog.2021.120",
journal = "Journal of Glaciology",
year = 2021,
pages = "1–14",
publisher = "Cambridge University Press"
}
@article{IGM-inv,
author = "Jouvet, Guillaume",
title = "Inversion of a Stokes ice flow model emulated by deep learning",
DOI = "10.1017/jog.2022.41",
journal = "Journal of Glaciology",
year = "2022",
pages = "1--14",
publisher = "Cambridge University Press"
}