In the framework of flow simulations in Discrete Fracture Networks (DFN), we consider the problem of identifying backbones. Backbones of a DFN are subnetworks of fractures that preserve the main flow properties and can be fruitfully used to reduce the computational cost of simulations or to analyze clogging and waste storage problems. With a well-trained Neural Network for flux regression in a DFN at hand, we use the Layer-wise Relevance Propagation method to compute the expected relevance of each fracture to identify the backbone.

Discrete Fracture Network insights by eXplainable AI / Berrone, Stefano; Della Santa, Francesco; Mastropietro, Antonio; Pieraccini, Sandra; Vaccarino, Francesco. - (2020). ((Intervento presentato al convegno Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS).

Discrete Fracture Network insights by eXplainable AI

Berrone, Stefano;Della Santa, Francesco;Mastropietro, Antonio;Pieraccini, Sandra;Vaccarino, Francesco
2020

Abstract

In the framework of flow simulations in Discrete Fracture Networks (DFN), we consider the problem of identifying backbones. Backbones of a DFN are subnetworks of fractures that preserve the main flow properties and can be fruitfully used to reduce the computational cost of simulations or to analyze clogging and waste storage problems. With a well-trained Neural Network for flux regression in a DFN at hand, we use the Layer-wise Relevance Propagation method to compute the expected relevance of each fracture to identify the backbone.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2882079