Abstract
This research focuses on developing computational methods to estimate model parameters in resonant fluid-structure interaction problems over a wide frequency range by means of model inversion approaches. The considered problems are widely known to be subjected to local minima, which represent a major challenge in the field of parameter identification. In the proposed method, the frequency spectrum is divided into successive substeps allowing to efficiently guide the estimation towards the global minimum, i.e., the true model parameters. The estimation is performed through two frameworks, namely, the deterministic using gradient-based optimization and Bayesian using Markov chain Monte Carlo method. Proposed numerical examples illustrate the effectiveness and potential of the proposed stepwise scheme to find the global minimum and reduce the overall computational burden.
Published Version
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