Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
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Abstract (arXiv)
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO), for time-dependent forward modelling in groundwater systems. Through testing on synthetic datasets, it is demonstrated that U-Net and U-Net + ViT models outperform FNO in accuracy and efficiency, especially in sparse data scenarios. These findings underscore the potential of U-Net-based models for groundwater modelling in real-world applications where data scarcity is prevalent.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 8 Jul 2023.
Builds on
14 of 21 references.
References · 14
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.