Bayesian Structural Identification Using Gaussian Process Discrepancy

Bayesian Structural Identification Using Gaussian Process Discrepancy A novel bayesian framework is proposed for structural model updating, response predictions, and missing samples reconstruction using gp models. kernel functions are introduced for modeling the correlation of prediction errors. View a pdf of the paper titled bayesian structural identification using gaussian process discrepancy models, by antonina m. kosikova and 3 other authors.

Deep Gaussian Process Based Bayesian Inference For Contaminant Source This paper presents a new bayesian model updating approach, which describes the discrepancy between the model and structural responses through gaussian process (gp) models. Bayesian model updating based on gaussian process (gp) models has received attention in recent years, which incorporates kernel based gps to provide enhanced fidelity response predictions. In many engineering applications, missing data during system identification can hinder the performance of the identified model. in this paper, a novel two‐stage nonparametric framework is proposed…. Read the article bayesian structural identification using gaussian process discrepancy models on r discovery, your go to avenue for effective literature search.

A Fully Automated Framework Integrating Gaussian Process Regression And In many engineering applications, missing data during system identification can hinder the performance of the identified model. in this paper, a novel two‐stage nonparametric framework is proposed…. Read the article bayesian structural identification using gaussian process discrepancy models on r discovery, your go to avenue for effective literature search. Following this approach, this article investigates a methodology where the modeling error between the two stages is incorporated with gaussian distributions whose statistical parameters are also updated with available data. In this work, a two stage bayesian formulation for structural system identification considering modeling errors of natural frequencies and mode shapes has been developed. Article "bayesian structural identification using gaussian process discrepancy models" detailed information of the j global is an information service managed by the japan science and technology agency (hereinafter referred to as "jst").