Ice-flow model emulator based on physics-informed deep learning

• Published in: Journal of Glaciology
• Main Authors: Guillaume Jouvet, Guillaume Cordonnier
• Format: Article in Journal/Newspaper
• Language: English
• Published: Cambridge University Press
• Subjects: glacier flow; glacier modelling; glacier mechanics; ice-sheet modelling; Environmental sciences; GE1-350; Meteorology. Climatology; QC851-999; Ice Sheet; Ice Shelf; Journal of Glaciology
• Online Access: https://doi.org/10.1017/jog.2023.73; https://doaj.org/article/4a5a316da97843df8b6d9bd5b32a17c6

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.