Parameter estimation in modelling frequency response of coupled systems using a stepwise approach

TL;DR

This paper introduces a stepwise frequency subdivision method for parameter estimation in acoustic fluid-structure interaction problems, combining gradient-based and Bayesian frameworks to improve accuracy and uncertainty assessment.

Contribution

It proposes a novel stepwise approach to overcome local minima in frequency spectrum analysis, integrating deterministic and Bayesian inversion methods for enhanced parameter estimation.

Findings

Method effectively estimates parameters with controlled noise.

Bayesian framework provides uncertainty quantification.

Approach overcomes local minima challenges in full spectrum analysis.

Abstract

This paper studies the problem of parameter estimation in resonant, acoustic fluid-structure interaction problems over a wide frequency range. Problems with multiple resonances are known to be subjected to local minima, which represents a major challenge in the field of parameter identification. We propose a stepwise approach consisting in subdividing the frequency spectrum such that the solution to a low-frequency subproblem serves as the starting point for the immediately higher frequency range. In the current work, two different inversion frameworks are used. The first approach is a gradient-based deterministic procedure that seeks the model parameters by minimising a cost function in the least squares sense and the second approach is a Bayesian inversion framework. The latter provides a potential way to assess the validity of the least squares estimate. In addition, it presents several advantages by providing invaluable information on the uncertainty and correlation between the estimated parameters. The methodology is illustrated on synthetic measurements with known design variables and controlled noise levels. The model problem is deliberately kept simple to allow for extensive numerical experiments to be conducted in order to investigate the nature of the local minima in full spectrum analyses and to assess the potential of the proposed method to overcome these. Numerical experiments suggest that the proposed methods may present an efficient approach to find material parameters and their uncertainty estimates with acceptable accuracy.