
This paper presents a novel approach for stochastic nonlinear model updating in structural dynamics using backbone curves within a Bayesian framework integrated with Markov Chain Monte Carlo (MCMC) sampling.


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stochastic-nonlinear-model-updating-using-backbone-curves-and-bayesian-inference-with-markov-chain-monte-carlo


Stochastic Nonlinear Model Updating Using Backbone Curves and Bayesian Inference with Markov Chain Monte Carlo


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This paper introduces a novel, efficient method for stochastic model updating that leverages latent space representation learned by a Variational Autoencoder (VAE) to quantify uncertainties in engineering systems, especially when data and simulations are limited.


stochastic-model-updating-using-latent-space-representation-for-efficient-uncertainty-quantification


Stochastic Model Updating Using Latent Space Representation for Efficient Uncertainty Quantification
