@Article{IJNAM-22-1,
author = {Chawla , KapilHolmes , William and Temam , Roger},
title = {From Obstacle Problems to Neural Insights: Feedforward Neural Network Modeling of Ice Thickness},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2024},
volume = {22},
number = {1},
pages = {1--20},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2617-8710},
doi = {https://doi.org/10.4208/ijnam2025-1001},
url = {http://global-sci.org/intro/article_detail/ijnam/23564.html}
}