Search Results for author:
Found 3 papers, 0 papers with code
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes.
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
This new data leads to a Bayesian update of the parameters by the KF, which is used to enhance the state representation.
Calibrating constitutive models with full-field data via physics informed neural networks
The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
