arXiv subjects

Martin Špetlík

Publications and source records attributed to Martin Špetlík.

2 recordsLinked to original sources

arXiv2026

Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling

Modeling groundwater flow in three-dimensional fractured crystalline media requires accounting for strong spatial heterogeneity induced by fractures. Fine-scale discrete fracture-matrix (DFM) simulations can capture this complexity but are computationally expensive, especially when repeated evaluations are needed. To address this, we aim to employ a multilevel Monte Carlo (MLMC) framework in which numerical homogenization is used to upscale sub-resolution fracture effects when transitioning between accuracy levels. To reduce the cost of conventional 3D numerical homogenization, we develop a surrogate model that predicts the equivalent hydraulic conductivity tensor Keq from a voxelized 3D domain representing tensor-valued random fields of matrix and fracture conductivities. Fracture size, orientation, and aperture are sampled from distributions informed by natural observations. The surrogate architecture combines a 3D convolutional neural network with feed-forward layers, enabling it to capture both local spatial features and global interactions. Three surrogates are trained on data generated by DFM simulations, each corresponding to a different fracture-to-matrix conductivity contrast. Performance is evaluated across a wide range of fracture network parameters and matrix-field correlation lengths. The trained models achieve high accuracy, with normalized root-mean-square errors below 0.22 across most test cases. Practical applicability is demonstrated by comparing numerically homogenized conductivities with surrogate predictions in two macro-scale problems: computing equivalent conductivity tensors and predicting outflow from a constrained 3D domain. In both cases, surrogate-based upscaling preserves accuracy while substantially reducing computational cost, achieving speedups exceeding 100x when inference is performed on a GPU.

cs.LG↗

arXiv2024

Deep learning surrogate for predicting hydraulic conductivity tensors from stochastic discrete fracture-matrix models

Simulating 2D flow in fractured crystalline rock requires 2D stochastic discrete-fracture matrix (DFM) models. To obtain the simulation statistics of interest at an affordable computational cost, we aim to use the multilevel Monte Carlo method. To use this multiscale approach, one needs to upscale the hydraulic conductivity of the fractures by numerical homogenization. In this work, we substitute numerical homogenization with a surrogate model to speed up the computations. In particular, we resort to a deep convolutional neural network (CNN) connected to a deep feed-forward neural network. The equivalent hydraulic conductivity tensor $K_{eq}$ is predicted based on an input spatial random field (SRF) of hydraulic conductivity tensors, cross-section, and hydraulic conductivity of fractures. Three independent surrogates with the same architecture are trained using data from DFM models with three different ratios of hydraulic conductivities of fracture and bulk $K_f/K_b$. As the $K_f/K_b$ ratio increases, the multivariate $K_{eq}$ distribution becomes more complex, and thus, the prediction accuracy of the trained surrogates deteriorates. Regardless of $K_f/K_b$, however, an improvement in the prediction accuracy of the trained surrogates is noted as the considered fracture density of the modeling setup decreases. We also investigate prediction accuracy on input SRFs of different correlation lengths. Upscaling by numerical homogenization and by surrogate modeling is compared on two practical problems: upscaling of the hydraulic conductivity tensor and groundwater flow through a given surface. We obtained equally accurate results for the equivalent hydraulic tensor calculation of upscaled DFM models regardless of the upscaling method. For the groundwater flow problem, the accuracy of quantity of interest imitates the accuracy of $K_{eq}$ predictions.

math.AP↗