Ice-flow model emulator based on physics-informed deep learning
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 general...
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Online Access: | https://hal.science/hal-04232949 https://hal.science/hal-04232949/document https://hal.science/hal-04232949/file/PINN_ice_flow_emulator.pdf https://doi.org/10.1017/jog.2023.73 |
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ftccsdartic:oai:HAL:hal-04232949v1 2023-11-12T04:18:55+01:00 Ice-flow model emulator based on physics-informed deep learning Jouvet, Guillaume Cordonnier, Guillaume Université de Lausanne = University of Lausanne (UNIL) 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) 2023 https://hal.science/hal-04232949 https://hal.science/hal-04232949/document https://hal.science/hal-04232949/file/PINN_ice_flow_emulator.pdf https://doi.org/10.1017/jog.2023.73 en eng HAL CCSD International Glaciological Society info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2023.73 hal-04232949 https://hal.science/hal-04232949 https://hal.science/hal-04232949/document https://hal.science/hal-04232949/file/PINN_ice_flow_emulator.pdf doi:10.1017/jog.2023.73 info:eu-repo/semantics/OpenAccess ISSN: 0022-1430 EISSN: 1727-5652 Journal of Glaciology https://hal.science/hal-04232949 Journal of Glaciology, 2023, pp.1-15. ⟨10.1017/jog.2023.73⟩ [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] info:eu-repo/semantics/article Journal articles 2023 ftccsdartic https://doi.org/10.1017/jog.2023.73 2023-10-14T22:31:42Z 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 Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Journal of Glaciology 1 15 |
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Open Polar |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
[SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
spellingShingle |
[SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Jouvet, Guillaume Cordonnier, Guillaume Ice-flow model emulator based on physics-informed deep learning |
topic_facet |
[SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
description |
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. |
author2 |
Université de Lausanne = University of Lausanne (UNIL) 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) |
format |
Article in Journal/Newspaper |
author |
Jouvet, Guillaume Cordonnier, Guillaume |
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 |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04232949 https://hal.science/hal-04232949/document https://hal.science/hal-04232949/file/PINN_ice_flow_emulator.pdf https://doi.org/10.1017/jog.2023.73 |
genre |
Ice Shelf Journal of Glaciology |
genre_facet |
Ice Shelf Journal of Glaciology |
op_source |
ISSN: 0022-1430 EISSN: 1727-5652 Journal of Glaciology https://hal.science/hal-04232949 Journal of Glaciology, 2023, pp.1-15. ⟨10.1017/jog.2023.73⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2023.73 hal-04232949 https://hal.science/hal-04232949 https://hal.science/hal-04232949/document https://hal.science/hal-04232949/file/PINN_ice_flow_emulator.pdf doi:10.1017/jog.2023.73 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1017/jog.2023.73 |
container_title |
Journal of Glaciology |
container_start_page |
1 |
op_container_end_page |
15 |
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1782335431628554240 |