5 citations · 9 across the 5 of their papers we have counts for
5 papers
Equilibrium Conserving Neural Operators for Super-Resolution Learning
Vivek Oommen, Andreas E. Robertson, Daniel Diaz +5
Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-…
Mesh-based graph convolutional neural networks for modeling materials with microstructure
Ari Frankel, Cosmin Safta, Coleman Alleman +1
Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neur…
Sensitivity of void mediated failure to geometric design features of porous metals
Gregory H. Teichert, Mohammad Khalil, Coleman Alleman +2
Material produced by current metal additive manufacturing processes is susceptible to variable performance due to imprecise control of internal porosity, surface roughness, and con…
Modeling strength and failure variability due to porosity in additively manufactured metals
M. Khalil, G. H. Teichert, C. Alleman +4
To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quanti…
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…