7 citations · 9 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing
Francis Ogoke, Kyle Johnson, Michael Glinsky +3
Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control. Howe…
Calibrating constitutive models with full-field data via physics informed neural networks
Craig M. Hamel, Kevin N. Long, Sharlotte L. B. Kramer
The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper,…