ODINN.jl: Scientific machine learning glacier modelling

HAL 2026 Preprint Open access

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Authors: Jordi Bolíbar, Facundo Sapienza, Alban Gossard, Mathieu le Séac'h, Lucille Gimenes, Vivek Gajadhar, Fabien Maussion, Ching‐Yao Lai, Bert Wouters, Fernando Pérez
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References: 24

Abstract

`ODINN.jl` is a glacier model leveraging scientific machine learning (SciML) methods to perform forward and inverse simulations of large-scale glacier evolution. It can simulate both surface mass balance and ice flow dynamics through a modular architecture which enables the user to easily modify model components.

Fields of Study & Subjects

Cryospheric studies and observations Model Reduction and Neural Networks Landslides and related hazards

Publication details

Journal / source
HAL
Publication date
26 January 2026
Language
EN
Document type
Preprint