A robust multi-level data-driven Bayesian approach for nonlinear aeroelastic system identification

Michael McGurk

doi:10.1016/j.jsv.2026.120144

A robust multi-level data-driven Bayesian approach for nonlinear aeroelastic system identification

2026-09-26

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DOI：10.1016/j.jsv.2026.120144

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• Introduced a multi-level Bayesian approach for stochastic model identification. • Developed a framework quantifying epistemic uncertainty in data-driven models. • Proposed two novel methodologies for level convergence in multi-level Bayesian models. • Achieved major reduction in training data for nonlinear aeroelastic identification.

Keywords:

Bayesian model updating

Multi-fidelity data driven modeling

Nonlinear dynamics

Polymorphic uncertainty quantification

Stability analysis

Limit cycle oscillation

A robust multi-level data-driven Bayesian approach for nonlinear aeroelastic system identification

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