AI Model Speeds Up Groundwater Flow Simulations 100x

Researchers developed a convolutional neural network surrogate to predict hydraulic conductivity in fractured rock formations, reducing computational costs of complex 3D groundwater simulations by over 100 times. The model learns from discrete fracture-matrix simulations and maintains high accuracy across varying fracture networks, enabling faster evaluation of groundwater flow in heterogeneous subsurface environments.