ODINN.jl: A new modular, hybrid, differentiable glacier model

The representation of glacier ice flow dynamics in glacier models in response to climate change, such as basal sliding or rheology, remains a critical challenge, particularly in integrating mechanistic models based on differential equations with the growing availability of observational data. Here, we present ODINN.jl, an open-source, modular framework for hybrid glacier modelling that combines scientific machine learning (SciML) with physical process-based approaches based on partial differential equations (PDEs). The framework is designed to facilitate both forward and inverse simulations of glacier evolution, enabling the assimilation of diverse datasets—such as ice thickness, surface velocities, and climate reanalyses—into a unified modelling ecosystem.

We have recently released ODINN.jl v1.0 after almost 5 years of work, providing a new architecture and stable API structured as an interconnected suite of Julia packages, each addressing specific tasks: Sleipnir.jl for data management, Muninn.jl for surface mass balance, Huginn.jl for ice flow dynamics, and ODINN.jl itself as the SciML interface for differentiation, optimisation, and hybrid modelling. This architecture allows users to easily customise model components, swap physical parametrisations, and integrate data-driven models (e.g., neural networks) to represent sub-grid processes or empirical laws. A key feature of this framework is its capacity to leverage automatic differentiation and adjoint methods to optimise model parameters, initial conditions, and statistical regressors. Parallelization is available for both forward simulations and advanced inverse methods, such as universal differential equations (UDEs), to explore poorly understood processes like basal sliding or calving. An early prototype of the model showed its potential to learn hidden laws in a noisy synthetic dataset, and with this new stable release we are now moving to large-scale applications using regional remote sensing and field observations such as high-resolution ice surface velocities and ice thickness. 

ODINN.jl is compatible with the Open Global Glacier Model (OGGM) ecosystem, enabling simulations for virtually any glacier worldwide using preprocessed datasets (e.g., RGI outlines, DEMs, climate reanalyses). This new modelling framework offers a reproducible, open-source solution to bridge the gap between physical understanding and data-driven discovery. Through modularity, scalability, and open-source collaborative approaches, ODINN.jl aims to explore both methodological advancements and large-scale applied modelling in glaciology.