Deep Learning for Groundwater Modelling - Maria Taccari
LIFD 5th Celebration Event | Maria Taccari | 25th January 2024
This talk by Maria Luisa Taccari, a final-year PhD candidate from the University of Leeds, delves into the development of deep learning surrogate models in groundwater flow simulation. Surrogate models serve as efficient approximations of complex numerical groundwater models, like MODFLOW, which are traditionally crucial for water resource management but come with high computational demands. Through her research, Maria Luisa has illustrated that deep learning surrogate models, encompassing standard computer vision models, transformers, and neural operators, can substantially reduce computational demands. These data-driven methods have shown the potential to mimic the capabilities of numerical techniques accurately and efficiently. Finally, the talk also touches upon the application of these models with real-world data, which present unique challenges such as accounting for time-dependent problems, sparse data, and true-world variability.
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