Deep learning speeds up ice flow modelling by several orders of magnitude

• Published in: Journal of Glaciology
• Main Authors: Jouvet, Guillaume, Cordonnier, Guillaume, Kim, Byungsoo, Lüthi, Martin, Vieli, Andreas, Aschwanden, Andy
• Other Authors: Department of Geography Zürich, Universität Zürich Zürich = University of Zurich (UZH), GRAPHics and DEsign with hEterogeneous COntent (GRAPHDECO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology Zürich (ETH Zürich), Geophysical Institute Fairbanks, University of Alaska Fairbanks (UAF)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2021
• Subjects: [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; Journal of Glaciology
• Online Access: https://hal.inria.fr/hal-03525458; https://hal.inria.fr/hal-03525458/document; https://hal.inria.fr/hal-03525458/file/JOG-21-0059.pdf; https://doi.org/10.1017/jog.2021.120

International audience This paper introduces the Instructed Glacier Model (IGM) – a model that simulates ice dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice sheets. The novelty of IGM is that it models the ice flow by a Convolutional Neural Network, which is trained from data generated with hybrid SIA + SSA or Stokes ice flow models. By doing so, the most computationally demanding model component is substituted by a cheap emulator. Once trained with representative data, we demonstrate that IGM permits to model mountain glaciers up to 1000 × faster than Stokes ones on Central Processing Units (CPU) with fidelity levels above 90% in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to the GPU often permits additional significant speed-ups, especially when emulating Stokes dynamics or/and modelling at high spatial resolution. IGM is an open-source Python code which deals with two-dimensional (2-D) gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, highly efficient and mechanically state-of-the-art glacier and icefields simulations.