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Extensions of Dynamic Programming, Machine Learning, Discrete Optimization
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Extensions of Dynamic Programming, Machine Learning, Discrete Optimization
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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
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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.
