Calibrating constitutive models with full-field data via physics informed neural networks. - Craig M. Hamel; Kevin N. Long; Sharlotte L. B. Kramer (CoRR, 2022)

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter. - Rubén Villarreal; Nikolaos N. Vlassis; Nhon N. Phan; Tommie A. Catanach; Reese E. Jones; Nathaniel A. Trask; Sharlotte L. B. Kramer; WaiChing Sun (CoRR, 2022)

Uncertainty quantification of neural network models of evolving processes via Langevin sampling. - Cosmin Safta; Reese E. Jones; Ravi G. Patel; Raelynn Wonnacott; Dan S. Bolintineanu; Craig M. Hamel; Sharlotte L. B. Kramer (CoRR, 2025)