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Citing IGM

Primary reference

When using IGM in your research, please cite the model development paper:

Jouvet, G., Finley, B. D., Gregov, T., Rosier, S. H. R., Leger, T. P. M., Bacchin, L., Cook, S., Frank, T., Henz, A., Herrmann, O., Maussion, F., Smith, G. M. A., & Stucki, C.-M. (2026). IGM v3.2.1: an accessible, modular, differentiable, and GPU-accelerated high-order ice flow model. EarthArXiv [preprint], submitted to Geoscientific Model Development. doi:10.31223/X5GB6G

@article{IGM,
  title   = {{IGM} v3.2.1: an accessible, modular, differentiable, and {GPU}-accelerated high-order ice flow model},
  author  = {Jouvet, Guillaume and Finley, Brandon D. and Gregov, Thomas and Rosier, Sebastian H. R.
             and Leger, Tancr{\`e}de P. M. and Bacchin, Lucie and Cook, Samuel and Frank, Thomas
             and Henz, Andreas and Herrmann, Oskar and Maussion, Fabien and Smith, Gillian M. A.
             and Stucki, Claire-Mathilde},
  journal = {EarthArXiv [preprint]},
  note    = {Submitted to Geoscientific Model Development},
  year    = {2026},
  doi     = {10.31223/X5GB6G},
}

This entry will be updated with the journal reference once the paper is published.

Foundational papers

The approach underlying IGM builds on a line of methodological papers. Depending on which features you use, you may also wish to cite:

Physics-informed ice flow solver:

Jouvet, G., & Cordonnier, G. (2023). Ice-flow model emulator based on physics-informed deep learning. Journal of Glaciology, 69(278), 1941–1955. doi:10.1017/jog.2023.73

@article{IGM-pinn,
  author  = {Jouvet, Guillaume and Cordonnier, Guillaume},
  title   = {Ice-flow model emulator based on physics-informed deep learning},
  journal = {Journal of Glaciology},
  year    = {2023},
  volume  = {69},
  number  = {278},
  pages   = {1941--1955},
  doi     = {10.1017/jog.2023.73},
}

Inversion / data assimilation:

Jouvet, G. (2023). Inversion of a Stokes glacier flow model emulated by deep learning. Journal of Glaciology, 69(273), 13–26. doi:10.1017/jog.2022.41

@article{IGM-inv,
  author  = {Jouvet, Guillaume},
  title   = {Inversion of a {Stokes} ice flow model emulated by deep learning},
  journal = {Journal of Glaciology},
  year    = {2023},
  volume  = {69},
  number  = {273},
  pages   = {13--26},
  doi     = {10.1017/jog.2022.41},
}

Data-driven ice flow emulator:

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. doi:10.1017/jog.2021.120

@article{IGM-data-driven,
  author  = {Jouvet, Guillaume and Cordonnier, Guillaume and Kim, Byungsoo
             and L{\"u}thi, Martin and Vieli, Andreas and Aschwanden, Andy},
  title   = {Deep learning speeds up ice flow modelling by several orders of magnitude},
  journal = {Journal of Glaciology},
  year    = {2022},
  volume  = {68},
  number  = {270},
  pages   = {651--664},
  doi     = {10.1017/jog.2021.120},
}