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Our manuscript on using deep learning to perform recursive homogenization for multi-permeability materials accepted by CMAME.
Our manuscript on using recurrent neural network to perform offline homogenization for multi-phase multi-permeability porous media has been accepted by CMAME today. This technique break down the computational barrier commonly exhibited in DEM-FEM and FEM2 models and therefore allow simulations connected across multiple scales. Spectral decomposition is used to correct the frame-dependent issues exhibited in RNN constitutive laws; issues on over- and under-fitting are regularized; k-fold validation techniques are used; and a model selection procedure on a directed graph is introduced. [PDF]
News about Computational Poromechanics lab at Columbia University.