A non-intrusive neural-network based BFGS algorithm for parameter estimation in non-stationary elasticity
Numerical Analysis
2024-08-19 v2 Numerical Analysis
Optimization and Control
Abstract
We present a non-intrusive gradient and a non-intrusive BFGS algorithm for parameter estimation problems in non-stationary elasticity. To avoid multiple (and potentially expensive) solutions of the underlying partial differential equation (PDE), we approximate the PDE solver by a neural network within the algorithms. The network is trained offline for a given set of parameters. The algorithms are applied to an unsteady linear-elastic contact problem; their convergence and approximation properties are investigated numerically.
Cite
@article{arxiv.2312.17373,
title = {A non-intrusive neural-network based BFGS algorithm for parameter estimation in non-stationary elasticity},
author = {Stefan Frei and Jan Reichle and Stefan Volkwein},
journal= {arXiv preprint arXiv:2312.17373},
year = {2024}
}