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

Search by :

ALL Author Subject ISBN/ISSN Advanced Search

Last search:

Image of Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos

Text

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

JANYA-ANURAK, Chettapong - Personal Name;

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.

Availability

No copy data

Detail Information

Series Title: -
Call Number: 005 JAN f
Publisher: : KIT Scientific Publishing., 2017
Collation: -
Language: English
ISBN/ISSN: 978-3-7315-0642-3
Classification: 005
Content Type: text
Media Type: computer
Carrier Type: online resource
Edition: -
Subject(s): Computer Science
Specific Detail Info: -
Statement of Responsibility: Chettapong Janya-anurak

Other Information

Cataloger: Kurnadi
Source: -
Validator: maya

Other version/related

No other version available

File Attachment

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

Comments

You must be logged in to post a comment