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We present a non-intrusive gradient algorithm for parameter estimationproblems in non-stationary elasticity. To avoid multiple (and potentiallyexpensive) solutions of the underlying partial differential equation (PDE), weapproximate the PDE solver by a neural network within the gradient algorithm.The network is trained offline for a given set of parameters. The algorithm isapplied to an unsteady linear-elastic contact problem; its convergence andapproximation properties are investigated numerically.

A non-intrusive neural-network based gradient algorithm for parameter estimation in non-stationary elasticity