Bayesian Model Updating Using Hybrid Monte Carlo Simulation with Application to Structural Dynamic Models with Many Uncertain Parameters

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Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018, methodology): “This methodology has been used extensively in the past for model calibration, prediction and selection [42, 43, 44, 10].”
Bayesian system identification based on probability logic (2010, methodology): “…updating of linear structural models for structural health monitoring using changes in modal parameter estimates [7,23], Bayesian updating and model class assessment of unidentiﬁable hysteretic structural models [8] and of dynamic structural models with a large number of uncertain parameters [25].”
Bayesian Updating with Structural Reliability Methods (2015, methodology): “Many authors have applied and adopted MCMC to Bayesian updating of mechanical models, including (Beck and Au 2002; Cheung and Beck 2009; Sundar and Manohar 2013).”
A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites (2010, methodology): “A general Bayesian approach to structural dynamics problems was put forth in the ’90s with works by Beck and Katafygiotis [11,12].”