Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling.

This paper presents a comprehensive comparison of various machine learning models, namely U-Net (Ronneberger et al., 2015), U-Net integrated with Vision Transformers (ViT) (Dosovitskiy et al., 2021), and Fourier Neural Operator (FNO) (Li et al., 2020), 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.