70 citations · 80 across the 7 of their papers we have counts for
3 papers · 1 filter
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…