Exploring the RCNN Technique in a Multiple-Point Statistics Framework
Ortiz, Julian M.
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This work explores the Recursive Convolutional Neural Network (RCNN) technique in terms of describing (1) the inner architecture, (2) the training process and (3) the simulation algorithm in a multiple-point statistics simulation framework. To acquire a more intuitive understanding of the previous description, visualizations of hidden layers activations using three different training images is presented. Two interesting applications (1) using RCNN E-types as secondary information for MPS algorithms and (2) training RCNN with scarce and limited information, are also explored.