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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…
cs.LG2022★ 44 cited
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
Nikolaos N. Vlassis, WaiChing Sun
The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how th…