Data‐Driven Inference of the Mechanics of Slip Along Glacier Beds Using Physics‐Informed Neural Networks: Case Study on Rutford Ice Stream, Antarctica
Abstract 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 learni...
Saved in:
Main Authors: | B. Riel, B. Minchew, T. Bischoff |
---|---|
Format: | article |
Language: | EN |
Published: |
American Geophysical Union (AGU)
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/6cf18a304d24415f8fc32bc86dd9c0a8 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Simulating Linear Kinematic Features in Viscous‐Plastic Sea Ice Models on Quadrilateral and Triangular Grids With Different Variable Staggering
by: C. Mehlmann, et al.
Published: (2021) -
Analysis of and Solution to the Polar Numerical Noise Within the Shallow‐Water Model on the Latitude‐Longitude Grid
by: Jianghao Li, et al.
Published: (2020) -
Realistic Simulation of Tropical Atmospheric Gravity Waves Using Radar‐Observed Precipitation Rate and Echo Top Height
by: Martina Bramberger, et al.
Published: (2020) -
Changes to the Madden‐Julian Oscillation in Coupled and Uncoupled Aquaplanet Simulations With 4xCO2
by: Hien X. Bui, et al.
Published: (2020) -
The Role of Isotope‐Enabled GCM Complexity in Simulating Tropical Circulation Changes in High‐CO2 Scenarios
by: Jun Hu, et al.
Published: (2020)