65 citations · 98 across the 10 of their papers we have counts for
12 papers
Learning hyperelastic anisotropy from data via a tensor basis neural network
Jan N. Fuhg, Nikolaos Bouklas, Reese E. Jones
Anisotropy in the mechanical response of materials with microstructure is common and yet is difficult to assess and model. To construct accurate response models given only stress-s…
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…
Tensor Basis Gaussian Process Models of Hyperelastic Materials
Ari Frankel, Reese Jones, Laura Swiler
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy…
Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model
Ari Frankel, Kousuke Tachida, Reese Jones
Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate. Using a neural network with conv…
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…
Atomic-scale interaction of a crack and an infiltrating fluid
R. E. Jones, W. C. Tucker, J. M. Rimsza +1
In this work we investigate the Orowan hypothesis, that decreases in surface energy due to surface adsorbates lead directly to lowered fracture toughness, at an atomic/molecular le…
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…
The influence of defects on the thermal conductivity of compressed LiF
Reese E. Jones, Donald K. Ward
Defect formation in LiF, which is used as an observation window in ramp and shock experi- ments, has significant effects on its transmission properties. Given the extreme condition…
Comparison of dislocation density tensor fields derived from discrete dislocation dynamics and crystal plasticity simulations of torsion
Reese Jones, Jonathan Zimmerman, Giacomo Po
The importance of accurate simulation of the plastic deformation of ductile metals to the design of structures and components is well-known. Many techniques exist that address the…