4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2022
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…
cs.LG2022★ 2 cited
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…
cond-mat.mtrl-sci2019★ 4 cited
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…