English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Universal Differential Equations for glacier ice flow modelling using ODINN.jl

Bolibar, J., Sapienza, F., Maussion, F., Lguensat, R., Wouters, B., Pérez, F. (2023): Universal Differential Equations for glacier ice flow modelling using ODINN.jl, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3881

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020665 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020665_1
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Bolibar, Jordi1, Author
Sapienza, Facundo1, Author
Maussion, Fabien1, Author
Lguensat, Redouane1, Author
Wouters, Bert1, Author
Pérez, Fernando1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

Content

show
hide
Free keywords: -
 Abstract: Inversion methods play an important role in glacier models, both to calibrate and estimate parameters of interest (e.g. Glen's coefficients or basal sliding). However, inversions are usually made for each glacier individually, without using any global information, i.e. without deriving general laws governing the spatiotemporal variability of those parameters. The reason behind this limitation is twofold: the statistical challenge of making constrained inferences with multiple glaciers, and the computational limitation of processing massive glacier datasets. Machine learning powered with differential programming is a tool that can address both limitations. We introduce a statistical framework for functional inversion of physical processes governing global-scale glacier changes. We apply this framework to invert a prescribed function describing the spatial variability of Glen’s coefficient (A). Instead of estimating a single parameter per glacier, we learn the parameters of a regressor (i.e. a neural network) that encodes information related to each glacier (i.e. long-term air temperature) to the parameter of interest. The inversion is done by embedding a neural network inside the Shallow Ice Approximation PDE - resulting in a Universal Differential Equation - with the goal of minimizing the error on the simulated ice surface velocities. This framework is built inside ODINN.jl, an open-source package in the Julia programming language for global glacier evolution modelling using Universal Differential Equations. ODINN exploits the latest generation of ice surface velocities and geodetic mass balance remote sensing products, as well as many preprocessing tools from the Open Global Glacier Model (OGGM).

Details

show
hide
Language(s): eng - English
 Dates: 2023-07-112023-07-11
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.57757/IUGG23-3881
 Degree: -

Event

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

Legal Case

show

Project information

show

Source 1

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -