47 citations · 47 across the 3 of their papers we have counts for
7 papers
Point-Cloud Deep Learning of Porous Media for Permeability Prediction
Ali Kashefi, Tapan Mukerji
We propose a novel deep learning framework for predicting permeability of porous media from their digital images. Unlike convolutional neural networks, instead of feeding the whole…
A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries
Ali Kashefi, Davis Rempe, Leonidas J. Guibas
We present a novel deep learning framework for flow field predictions in irregular domains when the solution is a function of the geometry of either the domain or objects inside th…
A coarse-grid projection method for accelerating incompressible MHD flow simulations
Ali Kashefi
Coarse grid projection (CGP) is a multiresolution technique for accelerating numerical calculations associated with a set of nonlinear evolutionary equations along with the stiff P…
Spring-Slider and Finite Element Modeling of Microseismic Events and Fault Slip during Hydraulic Fracturing
Ali Kashefi, Eric M. Dunham, Benjamin Grossman-Ponemon +1
Hydraulic fracturing increases reservoir permeability by opening fractures and triggering slip on natural fractures and faults. While seismic slip of small faults or fault patches…
A coarse-grid incremental pressure-projection method for accelerating low Reynolds-number incompressible flow simulations
A. Kashefi
Coarse grid projection (CGP) multigrid techniques are applicable to sets of equations that include at least one decoupled linear elliptic equation. In CGP, the linear elliptic equa…
Coarse grid projection methodology: A partial mesh refinement tool for incompressible flow simulations
Ali Kashefi
We discuss Coarse Grid Projection (CGP) methodology as a guide for partial mesh refinement of incompressible flow computations for the first time. Based on it, if for a given spati…