IGM - a glacier evolution model
Project description
The Instructed Glacier Model (IGM)
The former IGM, which was hosted on this repo < 17/09/23, is accessible on this page.
Overview
The Instructed Glacier Model (IGM) is an open-source Python package, which permits to simulate 3D glacier evolution accounting for the coupling between ice thermo-dynamics, surface mass balance, and mass conservation. IGM features:
-
Simplicity and modularity: IGM is implemented in the most popular programming language -- Python -- at a low level of abstractivity. IGM is organized module-wise for clarity and to facilitate coupling, customization and commmunity development. For simplicity, IGM assumes a horizontal regular grid for numerical discretization and therefore deals with 2D gridded input and output data.
-
State-of-the-art physics: IGM implements mass conservation, high-order 3D ice flow mechanics, an Enthalpy model for the thermic regime of ice, melt/accumulation surface mass balance model, and other glaciological processes.
-
Computational high efficiency: Thanks to the TensorFlow library, mathematical operations are fully-vectorized. This permits tremendous speed-ups on GPU. Physics-informed deep learning is used as an alternative to numerical solvers for modelling ice flow physics in a vectorized way. While GPU are highly-recommended for modelling large domain / high resolution, IGM runs fairly well on CPU for individual glaciers.
-
Automatic differentiation: TensorFlow operations are differentiable. Therefore, automatic differentiation strongly facilitates and speeds-up inverse modelling / data assimilation.
Documentation
IGM's documentation can be found on the dedicated wiki.
Discord channel
IGM has a discord channel for quick support, getting latest infos, exchanges with other users. To get in, please send me your discord user name at guillaume.jouvet at unil.ch, and I will add you to the group.
Contact
Feel free to drop me an email for any questions, bug reports, or ideas of model extension: guillaume.jouvet at unil.ch
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file igm-model-2.0.4.tar.gz.
File metadata
- Download URL: igm-model-2.0.4.tar.gz
- Upload date:
- Size: 7.5 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fee047d3c49383e9371f155397134827b7492d6b11bc957f41b00fdcfdda3617
|
|
| MD5 |
d86495487d8a4f520334550876635815
|
|
| BLAKE2b-256 |
ac319db0d1225a4a5ed6dd004dcc0f9c86977811da2288995a28d5ffb44636b5
|
File details
Details for the file igm_model-2.0.4-py3-none-any.whl.
File metadata
- Download URL: igm_model-2.0.4-py3-none-any.whl
- Upload date:
- Size: 7.5 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b14ed1d2473f788dd1460092b6fd1122c2948c43d75f53be785e44ce30fe59ef
|
|
| MD5 |
213b1b277e8abeed1fb6269ebfb73787
|
|
| BLAKE2b-256 |
68595ba5f1bca2f18dd60080227be29e5e276eac128f89b58c0eb61fb513de78
|
