Understanding the Efficacy of U-Net & Vision Transformer for Groundwater
  Numerical Modelling
Maria Luisa Taccari Oded Ovadia He Wang Adar Kahana Xiaohui Chen Peter K. Jimack
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.