Data-Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics-Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Riel, B., Minchew, B., Bischoff, T.
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2021
• Subjects: Rutford; Rutford Ice Stream; Antarc*; Antarctica; Antarctica Journal
• Online Access: https://authors.library.caltech.edu/111478/; https://authors.library.caltech.edu/111478/3/2021MS002621.pdf; https://authors.library.caltech.edu/111478/4/2021ms002621-sup-0001-supporting%20information%20si-s01.pdf; https://resolver.caltech.edu/CaltechAUTHORS:20211015-222200700

Reliable projections of sea-level rise depend on accurate representations of how fast-flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or “sliding law”) whose proper form remains uncertain. Here, we present a novel deep learning-based...