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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| Format: | Article |
| Language: | English |
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Cambridge University Press
2023-12-01
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| Series: | Journal of Glaciology |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S0022143023000734/type/journal_article |
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| author | Guillaume Jouvet Guillaume Cordonnier |
| author_facet | Guillaume Jouvet Guillaume Cordonnier |
| author_sort | Guillaume Jouvet |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-4a5a316da97843df8b6d9bd5b32a17c6 |
| institution | Kabale University |
| issn | 0022-1430 1727-5652 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Glaciology |
| spelling | doaj-art-4a5a316da97843df8b6d9bd5b32a17c62024-12-11T10:15:40ZengCambridge University PressJournal of Glaciology0022-14301727-56522023-12-01691941195510.1017/jog.2023.73Ice-flow model emulator based on physics-informed deep learningGuillaume Jouvet0https://orcid.org/0000-0002-8546-8459Guillaume Cordonnier1https://orcid.org/0000-0003-0124-0180Université de Lausanne, IDYST, 1015 Lausanne, SwitzerlandInria, Université Côte d'Azur, Sophia-Antipolis, FranceConvolutional 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.https://www.cambridge.org/core/product/identifier/S0022143023000734/type/journal_articleglacier flowglacier modellingglacier mechanicsice-sheet modelling |
| spellingShingle | Guillaume Jouvet Guillaume Cordonnier Ice-flow model emulator based on physics-informed deep learning Journal of Glaciology glacier flow glacier modelling glacier mechanics ice-sheet modelling |
| title | 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_short | Ice-flow model emulator based on physics-informed deep learning |
| title_sort | ice flow model emulator based on physics informed deep learning |
| topic | glacier flow glacier modelling glacier mechanics ice-sheet modelling |
| url | https://www.cambridge.org/core/product/identifier/S0022143023000734/type/journal_article |
| work_keys_str_mv | AT guillaumejouvet iceflowmodelemulatorbasedonphysicsinformeddeeplearning AT guillaumecordonnier iceflowmodelemulatorbasedonphysicsinformeddeeplearning |