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

IF 2.8 3区 地球科学 Q2 GEOGRAPHY, PHYSICAL Journal of Glaciology Pub Date : 2023-09-26 DOI:10.1017/jog.2023.73
Guillaume Jouvet, Guillaume Cordonnier
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引用次数: 1

Abstract

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.
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基于物理信息深度学习的冰流模型仿真器
从高阶冰流模型实现中训练的卷积神经网络(CNN)在保真度和计算性能方面已被证明是出色的模拟器。然而,依赖于讲师模型的整体实现使得该策略难以推广到自然界中发现的各种冰流状态。为了克服这个问题,我们采用了基于物理的深度学习方法,它融合了基于有限差分/元素的传统数值解和深度学习方法。在这里,我们训练CNN在冰川演化模型的时间迭代中最小化与高阶冰流方程相关的能量。因此,我们的模拟器是传统求解器的一个有前途的替代品,这要归功于它的高计算效率(特别是在GPU上),对原始模型的高保真度,简化的训练(不需要任何数据),处理各种冰流状态和记忆以前的解决方案的能力,以及相对简单的实现。嵌入到“指示冰川模型”(IGM)框架中,仿真器的潜力通过三个应用程序进行说明,包括大规模高分辨率(2400x4000)正向冰川演化模型,数据同化的逆建模案例和冰架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Glaciology
Journal of Glaciology 地学-地球科学综合
CiteScore
5.80
自引率
14.70%
发文量
101
审稿时长
6 months
期刊介绍: Journal of Glaciology publishes original scientific articles and letters in any aspect of glaciology- the study of ice. Studies of natural, artificial, and extraterrestrial ice and snow, as well as interactions between ice, snow and the atmospheric, oceanic and subglacial environment are all eligible. They may be based on field work, remote sensing, laboratory investigations, theoretical analysis or numerical modelling, or may report on newly developed glaciological instruments. Subjects covered recently in the Journal have included palaeoclimatology and the chemistry of the atmosphere as revealed in ice cores; theoretical and applied physics and chemistry of ice; the dynamics of glaciers and ice sheets, and changes in their extent and mass under climatic forcing; glacier energy balances at all scales; glacial landforms, and glaciers as geomorphic agents; snow science in all its aspects; ice as a host for surface and subglacial ecosystems; sea ice, icebergs and lake ice; and avalanche dynamics and other glacial hazards to human activity. Studies of permafrost and of ice in the Earth’s atmosphere are also within the domain of the Journal, as are interdisciplinary applications to engineering, biological, and social sciences, and studies in the history of glaciology.
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