19 citations · 47 across the 5 of their papers we have counts for
6 papers
Predicting the mechanical response of oligocrystals with deep learning
Ari L. Frankel, Reese E. Jones, Coleman Alleman +1
In this work we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these…
Machine learning models of plastic flow based on representation theory
Reese E. Jones, Jeremy A. Templeton, Clay M. Sanders +1
We use machine learning (ML) to infer stress and plastic flow rules using data from repre- sentative polycrystalline simulations. In particular, we use so-called deep (multilayer)…
Bayesian Modeling of Inconsistent Plastic Response due to Material Variability
Francesco Rizzi, Mohammad Khalil, Reese E. Jones +3
The advent of fabrication techniques such as additive manufacturing has focused attention on the considerable variability of material response due to defects and other microstructu…
Plasticity models of material variability based on uncertainty quantification techniques
F. Rizzi, R. E. Jones, J. A. Templeton +2
The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structura…
Compressed Sensing and Reconstruction of Unstructured Mesh Datasets
Maher Salloum, Nathan Fabian, David M. Hensinger +1
Exascale computing promises quantities of data too large to efficiently store and transfer across networks in order to be able to analyze and visualize the results. We investigate…
Model Reduction with MapReduce-enabled Tall-and-Skinny Singular Value Decomposition
Paul G. Constantine, David F. Gleich, Yangyang Hou +1
We present a method for computing reduced-order models of parameterized partial differential equation solutions. The key analytical tool is the singular value expansion of the para…