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Article
Geography, Physical
Guillaume Jouvet
Summary: In this study, we address the challenge of data assimilation in high-order ice flow modeling by using a deep learning-based emulator. By substituting the ice flow equations with a convolutional neural network emulator, we simplify and accelerate the solving of the optimization problem, resulting in an accurate inference of ice thickness and ice flow parameter distributions.
JOURNAL OF GLACIOLOGY
(2023)
Article
Mathematics, Applied
P. Minakowski et al.
Summary: In this study, we analyze neural network solutions to partial differential equations obtained with Physics Informed Neural Networks. We apply classical finite element error analysis tools to investigate the error of the Deep Ritz method applied to the Laplace and the Stokes equations. Additionally, we propose an a posteriori error estimator based on the dual weighted residual estimator to ensure the accuracy of neural network approximations of partial differential equations.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Qizhi He et al.
Summary: In this study, a hybrid method is developed to approximate existing ice sheet computational models at a lower cost and improve the efficiency of uncertainty quantification. The results show that the hybrid model is highly accurate and faster than the traditional model. Additionally, the model can handle high-dimensional parameter spaces, making it suitable for generating samples for uncertainty quantification.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Geography, Physical
Guillaume Jouvet et al.
Summary: Using physics-informed deep learning, a CNN emulator is trained to minimize the energy associated with high-order ice-flow equations. The emulator offers high computational efficiency, fidelity to the original model, and capability to handle different ice-flow regimes.
JOURNAL OF GLACIOLOGY
(2023)
Article
Computer Science, Software Engineering
Guillaume Cordonnier et al.
Summary: We present a novel solution for simulating the formation and evolution of glaciers, as well as their erosive effects, in the context of glacial and inter-glacial cycles. Our solution includes a fast and accurate deep learning-based estimation method for high-order ice flows, as well as a new multi-scale advection scheme to handle the different time scales of glacier equilibrium and terrain erosion. By combining these methods, we are able to accurately model the formation of various terrain features, including U-shaped and hanging valleys, fjords, and glacial lakes, which were not adequately represented in previous computer graphics models.
ACM TRANSACTIONS ON GRAPHICS
(2023)
Article
Geography, Physical
Guillaume Jouvet et al.
Summary: This paper introduces a model called Instructed Glacier Model (IGM), which simulates ice dynamics using a Convolutional Neural Network and a cheap emulator trained from data. It can predict the evolution of glaciers accurately and efficiently.
JOURNAL OF GLACIOLOGY
(2022)
Article
Geography, Physical
Douglas Brinkerhoff et al.
Summary: The study highlights the importance of basal motion in Greenland ice flux, but notes the challenges in predicting it. By using a Bayesian approach and coupling models with observations, the study was able to infer parameter distributions. The findings show that surface velocity observations provide strong constraints on model parameters, but further data collection and model development are necessary for comprehensive understanding.
JOURNAL OF GLACIOLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
B. Riel et al.
Summary: Reconstructing the evolution of drag at glacier beds from time-dependent ice velocity and elevation observations using deep learning provides a new way to investigate the relationship between slip of glaciers and basal drag, without needing to specify the form of the sliding law. This approach integrates existing knowledge of ice flow physics with remote sensing data to improve flow models and provides insights into the mechanisms driving changes in ice flow dynamics.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2021)
Article
Geography, Physical
Helene Seroussi et al.
Article
Geosciences, Multidisciplinary
Ludovic Rass et al.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2020)
Article
Computer Science, Interdisciplinary Applications
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Geosciences, Multidisciplinary
Fabien Maussion et al.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2019)
Article
Mathematics
E. Weinan et al.
COMMUNICATIONS IN MATHEMATICS AND STATISTICS
(2018)
Article
Mechanics
G. Jouvet
JOURNAL OF FLUID MECHANICS
(2016)
Review
Multidisciplinary Sciences
Yann LeCun et al.
Article
Computer Science, Interdisciplinary Applications
Christian Fredborg Braedstrup et al.
COMPUTERS & GEOSCIENCES
(2014)
Review
Mechanics
Christian Schoof et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 45
(2013)
Article
Geography, Physical
F. Pattyn et al.
Article
Mechanics
Christian Schoof
JOURNAL OF FLUID MECHANICS
(2006)
