Ice-flow model emulator based on physics-informed deep learning
Abstract Convolutional neural networks (CNN) trained from high-order ice-flow model realisations have proven to be outstanding emulators in terms of fidelity and computational performance. However, the dependence on an ensemble of realisations of an instructor model renders this strategy difficult t...
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Online Access: | http://dx.doi.org/10.1017/jog.2023.73 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143023000734 |
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crcambridgeupr:10.1017/jog.2023.73 2024-10-06T13:49:38+00:00 Ice-flow model emulator based on physics-informed deep learning Jouvet, Guillaume Cordonnier, Guillaume 2023 http://dx.doi.org/10.1017/jog.2023.73 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143023000734 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology page 1-15 ISSN 0022-1430 1727-5652 journal-article 2023 crcambridgeupr https://doi.org/10.1017/jog.2023.73 2024-09-11T04:04:56Z Abstract Convolutional neural networks (CNN) trained from high-order ice-flow model realisations have proven to be outstanding emulators in terms of fidelity and computational performance. However, the dependence on an ensemble of realisations of an instructor model renders this strategy difficult to generalise to a variety of ice-flow regimes found in the nature. To overcome this issue, we adopt the approach of physics-informed deep learning, which fuses traditional numerical solutions by finite differences/elements and deep-learning approaches. Here, we train a CNN to minimise the energy associated with high-order ice-flow equations within the time iterations of a glacier evolution model. As a result, our emulator is a promising alternative to traditional solvers thanks to its high computational efficiency (especially on GPU), its high fidelity to the original model, its simplified training (without requiring any data), its capability to handle a variety of ice-flow regimes and memorise previous solutions, and its relatively simple implementation. Embedded into the ‘Instructed Glacier Model’ (IGM) framework, the potential of the emulator is illustrated with three applications including a large-scale high-resolution (2400x4000) forward glacier evolution model, an inverse modelling case for data assimilation, and an ice shelf. Article in Journal/Newspaper Ice Shelf Journal of Glaciology Cambridge University Press Journal of Glaciology 1 15 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
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English |
description |
Abstract Convolutional neural networks (CNN) trained from high-order ice-flow model realisations have proven to be outstanding emulators in terms of fidelity and computational performance. However, the dependence on an ensemble of realisations of an instructor model renders this strategy difficult to generalise to a variety of ice-flow regimes found in the nature. To overcome this issue, we adopt the approach of physics-informed deep learning, which fuses traditional numerical solutions by finite differences/elements and deep-learning approaches. Here, we train a CNN to minimise the energy associated with high-order ice-flow equations within the time iterations of a glacier evolution model. As a result, our emulator is a promising alternative to traditional solvers thanks to its high computational efficiency (especially on GPU), its high fidelity to the original model, its simplified training (without requiring any data), its capability to handle a variety of ice-flow regimes and memorise previous solutions, and its relatively simple implementation. Embedded into the ‘Instructed Glacier Model’ (IGM) framework, the potential of the emulator is illustrated with three applications including a large-scale high-resolution (2400x4000) forward glacier evolution model, an inverse modelling case for data assimilation, and an ice shelf. |
format |
Article in Journal/Newspaper |
author |
Jouvet, Guillaume Cordonnier, Guillaume |
spellingShingle |
Jouvet, Guillaume Cordonnier, Guillaume Ice-flow model emulator based on physics-informed deep learning |
author_facet |
Jouvet, Guillaume Cordonnier, Guillaume |
author_sort |
Jouvet, Guillaume |
title |
Ice-flow model emulator based on physics-informed deep learning |
title_short |
Ice-flow model emulator based on physics-informed deep learning |
title_full |
Ice-flow model emulator based on physics-informed deep learning |
title_fullStr |
Ice-flow model emulator based on physics-informed deep learning |
title_full_unstemmed |
Ice-flow model emulator based on physics-informed deep learning |
title_sort |
ice-flow model emulator based on physics-informed deep learning |
publisher |
Cambridge University Press (CUP) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1017/jog.2023.73 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143023000734 |
genre |
Ice Shelf Journal of Glaciology |
genre_facet |
Ice Shelf Journal of Glaciology |
op_source |
Journal of Glaciology page 1-15 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2023.73 |
container_title |
Journal of Glaciology |
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1 |
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15 |
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1812177695899910144 |