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@article{wang2022discovering,
title={Discovering the rheology of Antarctic Ice Shelves via physics-informed deep learning},
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title={Deep learning the flow law of Antarctic Ice Shelves},
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journal = {Under review},
year={2024}
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@article{MacAyeal1989,
author = {MacAyeal, Douglas R.},
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@article{Millstein2022ice,
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@article{ranganathan2024modified,
title={A modified viscous flow law for natural glacier ice: Scaling from laboratories to ice sheets},
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volume={121},
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}
@article{furst2015assimilation,
title={Assimilation of Antarctic velocity observations provides evidence for uncharted pinning points},
author={F{\"u}rst, JJ and Durand, G and Gillet-Chaulet, F and Merino, N and Tavard, L and Mouginot, J and Gourmelen, N and Gagliardini, O},
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volume={9},
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}
@article{morlighem2013inversion,
title={Inversion of basal friction in Antarctica using exact and incomplete adjoints of a higher-order model},
author={Morlighem, M and Seroussi, H and Larour, E and Rignot, E},
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@article{goldberg2011data,
title={Data assimilation using a hybrid ice flow model},
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@article{vieli2003application,
title={Application of control methods for modelling the flow of Pine Island Glacier, West Antarctica},
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@misc{Mouginot2019velo,
title={MEaSUREs Phase-Based Antarctica Ice Velocity Map, Version 1},
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year={2019}
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@misc{Morlighem2020thick,
title={MEaSUREs BedMachine Antarctica, Version 2},
url={https://nsidc.org/data/NSIDC-0756/versions/2},
publisher={NASA National Snow and Ice Data Center Distributed Active Archive Center},
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@article{lu2021deepxde,
title={DeepXDE: A deep learning library for solving differential equations},
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@article{daw2022mitigating,
title={Mitigating propagation failures in physics-informed neural networks using retain-resample-release (r3) sampling},
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title={Euler operators for mis-specified physics-informed neural networks},
author={Charlie Cowen-Breen and Yongji Wang and Stephen Bates and Ching-Yao Lai},
booktitle={ICML 2024 AI for Science Workshop},
year={2024},
url={https://openreview.net/forum?id=kkGR5fNq2J}
}
@article{riel2021data,
title={Data-Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics-Informed Neural Networks: Case Study on {R}utford {I}ce {S}tream, {A}ntarctica},
author={Riel, Bryan and Minchew, Brent and Bischoff, Tobias},
journal={Journal of Advances in Modeling Earth Systems},
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year={2021},
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@article{riel2023variational,
title={Variational inference of ice shelf rheology with physics-informed machine learning},
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@article{morlighem2010spatial,
title={Spatial patterns of basal drag inferred using control methods from a full-Stokes and simpler models for {P}ine {I}sland {G}lacier, {W}est {A}ntarctica},
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@article{iwasaki2023one,
title={One-dimensional ice shelf hardness inversion: Clustering behavior and collocation resampling in physics-informed neural networks},
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@article{jagtap2020extended,
title={Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations},
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@software{jax2018github,
author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
url = {http://github.com/google/jax},
version = {0.3.13},
year = {2018},
}