70 citations · 80 across the 7 of their papers we have counts for
8 papers
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
Jan N. Fuhg, Nikolaos Bouklas, Reese E. Jones
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easi…
Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations
Wyatt Bridgman, Reese Jones, Mohammad Khalil
For predictive modeling relying on Bayesian inversion, fully independent, or ``mean-field'', Gaussian distributions are often used as approximate probability density functions in v…
Modular machine learning-based elastoplasticity: generalization in the context of limited data
Jan N. Fuhg, Craig M. Hamel, Kyle Johnson +2
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges…
Deep learning and multi-level featurization of graph representations of microstructural data
Reese Jones, Cosmin Safta, Ari Frankel
Many material response functions depend strongly on microstructure, such as inhomogeneities in phase or orientation. Homogenization presents the task of predicting the mean respons…
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…
A heteroencoder architecture for prediction of failure locations in porous metals using variational inference
Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert +3
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities. Th…
A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables
R. E. Jones, A. L. Frankel, K. L. Johnson
We propose a neural network framework to preclude the need to define or observe incompletely or inaccurately defined states of a material in order to describe its response. The neu…
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…