Deep learning speeds up ice flow modelling by several orders of magnitude
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Date
2022-08
Publication Type
Journal Article
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yes
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Abstract
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 nov elty 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 com putationally 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
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Publication status
published
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Book title
Journal / series
Volume
68 (270)
Pages / Article No.
651 - 664
Publisher
Cambridge University Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Glacier flow; Glacier modelling; Ice dynamics; Ice velocity
Organisational unit
03420 - Gross, Markus / Gross, Markus
Notes
Funding
162444 - Modelling the ice flow in the western Alps during the last glacial cycle (SNF)