From Obstacle Problems to Neural Insights: Feedforward Neural Network Modeling of Ice Thickness
Year: 2025
Author: Kapil Chawla, William Holmes, Roger Temam
International Journal of Numerical Analysis and Modeling, Vol. 22 (2025), Iss. 1 : pp. 1–20
Abstract
In this study, we integrate the established obstacle problem formulation from ice sheet modeling [1, 2] with cutting-edge deep learning methodologies to enhance ice thickness predictions, specifically targeting the Greenland ice sheet. By harmonizing the mathematical structure with an energy minimization framework tailored for neural network approximations, our method’s efficacy is confirmed through both 1D and 2D numerical simulations. Utilizing the NSIDC-0092 dataset for Greenland [22] and incorporating bedrock topography for model pre-training, we register notable advances in prediction accuracy. Our research underscores the potent combination of traditional mathematical models and advanced computational techniques in delivering precise ice thickness estimations.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/ijnam2025-1001
International Journal of Numerical Analysis and Modeling, Vol. 22 (2025), Iss. 1 : pp. 1–20
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 20
Keywords: Neural networks ice thickness estimation obstacle problems feedforward neural networks mathematical modeling partial differential equations.
Author Details
Kapil Chawla Email
William Holmes Email
Roger Temam Email