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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19529 |
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| _version_ | 1866916711408599040 |
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| author | Chawla, Kapil Holmes, William Temam, Roger |
| author_facet | Chawla, Kapil Holmes, William Temam, Roger |
| contents | In this study, we integrate the established obstacle problem formulation from ice sheet modeling 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19529 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Obstacle Problems to Neural Insights: Feed Forward Neural Network Modeling of Ice Thickness Chawla, Kapil Holmes, William Temam, Roger Numerical Analysis In this study, we integrate the established obstacle problem formulation from ice sheet modeling 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 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. |
| title | From Obstacle Problems to Neural Insights: Feed Forward Neural Network Modeling of Ice Thickness |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2407.19529 |