# Physics-Informed Machine Learning On Polar Ice: A Survey

arXiv: https://arxiv.org/abs/2404.19536 · HTML: https://mlsanity.com/paper/2404.19536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 139 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Variational Physics-Informed Neural Networks For Solving Partial Differential Equations](https://mlsanity.com/paper/1912.00873/) (2019)
- [Physics-Informed Deep Neural Operator Networks](https://mlsanity.com/paper/2207.05748/) (2022)
- [Neural Inverse Operators for Solving PDE Inverse Problems](https://mlsanity.com/paper/2301.11167/) (2023)
- [The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?](https://mlsanity.com/paper/2103.09655/) (2021)
- [Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws](https://mlsanity.com/paper/2312.11176/) (2023)
- [Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks](https://mlsanity.com/paper/2009.00191/) (2020)
- [A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling](https://mlsanity.com/paper/2301.11402/) (2023)
- [Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach](https://mlsanity.com/paper/2306.13181/) (2023)
- [Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks](https://mlsanity.com/paper/2306.13690/) (2023)
- [Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting](https://mlsanity.com/paper/2210.08877/) (2022)
- [Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling](https://mlsanity.com/paper/2402.05291/) (2024)
- [Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity](https://mlsanity.com/paper/2311.00167/) (2023)
- [Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery](https://mlsanity.com/paper/2010.03712/) (2020)
- [Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference](https://mlsanity.com/paper/2006.12422/) (2020)
- [Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms](https://mlsanity.com/paper/2310.19574/) (2023)
- [Regression Networks For Calculating Englacial Layer Thickness](https://mlsanity.com/paper/2104.04654/) (2021)

## Abstract (arXiv)

The mass loss of the polar ice sheets contributes considerably to ongoing sea-level rise and changing ocean circulation, leading to coastal flooding and risking the homes and livelihoods of tens of millions of people globally. To address the complex problem of ice behavior, physical models and data-driven models have been proposed in the literature. Although traditional physical models can guarantee physically meaningful results, they have limitations in producing high-resolution results. On the other hand, data-driven approaches require large amounts of high-quality and labeled data, which is rarely available in the polar regions. Hence, as a promising framework that leverages the advantages of physical models and data-driven methods, physics-informed machine learning (PIML) has been widely studied in recent years. In this paper, we review the existing algorithms of PIML, provide our own taxonomy based on the methods of combining physics and data-driven approaches, and analyze the advantages of PIML in the aspects of accuracy and efficiency. Further, our survey discusses some current challenges and highlights future opportunities, including PIML on sea ice studies, PIML with different combination methods and backbone networks, and neural operator methods.