Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems Using Bayesian Uncertainty Quantification Based on Generalized Polynomial Chaos

en Limba Engleză Hardback – 8 oct 2020

In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.

This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

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