113 citations · 268 across the 13 of their papers we have counts for
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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…
cond-mat.mtrl-sci2019★ 113 cited
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
G. H. Teichert, A. R. Natarajan, A. Van der Ven +1
The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this wo…