Machine learning for flux regression in discrete fracture networks

DOI10.1007/s13137-021-00176-0zbMath1476.65291OpenAlexW2942263784MaRDI QIDQ2059207

Stefano Berrone, Francesco Vaccarino, F. Della Santa, Sandra Pieraccini

Publication date: 13 December 2021

Published in: GEM - International Journal on Geomathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13137-021-00176-0

zbMATH Keywords

uncertainty quantificationdeep learningdiscrete fracture network flow simulations

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Reasoning under uncertainty in the context of artificial intelligence (68T37) Geophysics (86A99) Geophysical flows (76U60) Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)

Related Items (1)

Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake

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