Poster
in
Workshop: The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
Maria Luisa Taccari · Oded Ovadia · He Wang · Xiaohui Chen · Adar Kahana · Peter Jimack
Keywords: [ Vision Transformer ] [ U-Net ] [ Time-Dependent Forward Modelling ] [ Groundwater Modelling ]
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.