Machine learning for flux regression in discrete fracture networks
type: Item
instance of: scholarly article
title: Machine learning for flux regression in discrete fracture networks
zbMATH Open document ID: 1476.65291
author: S. Berrone
author: F. Della Santa
author: Francesco Vaccarino
author: Sandra Pieraccini
published in: GEM - International Journal on Geomathematics
publication date: 2021-12-13T00:00:00Z
Mathematics Subject Classification ID: 65N30
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 68T37
Mathematics Subject Classification ID: 76-10
Mathematics Subject Classification ID: 86A99
Mathematics Subject Classification ID: 76U60
zbMATH DE Number: 7443775
zbMATH Keywords: discrete fracture network flow simulations
zbMATH Keywords: deep learning
zbMATH Keywords: uncertainty quantification
MaRDI profile type: Publication
full work available at URL: s13137-021-00176-0
OpenAlex ID: W2942263784
cites work: Q3995194
cites work: The virtual element method for discrete fracture network simulations
cites work: A PDE-Constrained Optimization Formulation for Discrete Fracture Network Flows
cites work: On Simulations of Discrete Fracture Network Flows with an Optimization-Based Extended Finite Element Method
cites work: An optimization approach for large scale simulations of discrete fracture network flows
cites work: A Parallel Solver for Large Scale DFN Flow Simulations
cites work: Towards effective flow simulations in realistic discrete fracture networks
cites work: A Posteriori Error Estimate for a PDE-Constrained Optimization Formulation for the Flow in DFNs
cites work: Non-stationary transport phenomena in networks of fractures: effective simulations and stochastic analysis
cites work: Fast and robust flow simulations in discrete fracture networks with gpgpus
cites work: Q5483032
cites work: Uncertainty quantification of discontinuous outputs via a non-intrusive bifidelity strategy
cites work: A machine learning approach for efficient uncertainty quantification using multiscale methods
cites work: A Reduced Model for Flow and Transport in Fractured Porous Media with Non-matching Grids
cites work: Q5270493
cites work: Conforming Delaunay Triangulation of Stochastically Generated Three Dimensional Discrete Fracture Networks: A Feature Rejection Algorithm for Meshing Strategy
cites work: Modeling flow in porous media with fractures; Discrete fracture models with matrix-fracture exchange
cites work: Q4386878
cites work: On Information and Sufficiency
cites work: Divergence measures based on the Shannon entropy
cites work: A logical calculus of the ideas immanent in nervous activity
cites work: A quasi steady state method for solving transient Darcy flow in complex 3D fractured networks accounting for matrix to fracture flow
cites work: A quasi steady state method for solving transient Darcy flow in complex 3D fractured networks
cites work: Q5396710
cites work: A Mortar BDD Method for Solving Flow in Stochastic Discrete Fracture Networks
cites work: A mixed hybrid Mortar method for solving flow in discrete fracture networks
cites work: A generalized mixed hybrid mortar method for solving flow in stochastic discrete fracture networks
cites work: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
cites work: Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways
cites work: Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
DOI: 10.1007/S13137-021-00176-0
description: scientific article; zbMATH DE number 7443775