Skip to main content
King Abdullah University of Science and Technology
Bayesian Deep Learning
Bayesian Deep Learning

Main navigation

  • Home
  • People
    • All Profiles
    • Principal Investigators
    • Postdoctoral Fellows
  • Events
    • All Events
    • Events Calendar
  • News
  • Publications

discrete fracture network

Uncertainty quantification in Discrete Fracture Network models By Prof. Claudio Canuto (Dipartimento di Scienze Matematiche, Politecnico di Torino, Italy)

Prof. Claudio Canuto, Polytechnic of Turin, Italy

Feb 10, 15:00 - 16:00

B1 R4214

discrete fracture network

Discrete Fracture Network (DFN) models are widely used in the simulation of subsurface flows; they describe a geological reservoir as a system of many intersecting planar polygons representing the underground network of fractures. Among the different approaches to DFN simulations, recently (Berrone et al, 2013) a numerical model has been formulated as a PDE-constrained optimization problem, in which neither fracture/fracture nor fracture/trace mesh conformity is required.

Bayesian Deep Learning (BDL)

Footer

  • A-Z Directory
    • All Content
    • Browse Related Sites
  • Site Management
    • Log in

© 2025 King Abdullah University of Science and Technology. All rights reserved. Privacy Notice

Disclaimer: The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the King Abdullah University of Science and Technology.