Publications

Detailed Information

A model refinement framework for statistical model validation : 통계적 모델 검증을 위한 해석모델 개선 방법론

Cited 0 time in Web of Science Cited 0 time in Scopus
Authors

김지선

Advisor
윤병동
Major
공과대학 기계항공공학부
Issue Date
2013-02
Publisher
서울대학교 대학원
Keywords
Model refinementVerification and validation (V&V)Virtual testingModel Uncertainty
Description
학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 2. 윤병동.
Abstract
As the importance of virtual testing has been increased for cost-effective product design and design evaluation, researchers focus on studying validation and verification (V&V) to increase the computational model predictability. Model validation process can make the computational model accurately through the model calibration and validity check process
however, in some cases, unacknowledged uncertainties such as lack of knowledge and human mistakes still exist and decrease the predictability of the model. To overcome this challenge, this thesis presents a model refinement framework for statistical model validation. This framework consists of the three steps
1) invalidity analysis, 2) invalidity reasoning tree (IRT) and 3) invalidity sensitivity study. Invalidity analysis seeks possible causes for invalidity. Then, the IRT determines a parametric form of refinement candidates from the possible causes and invalidity sensitivity analysis finally checks the effect of the candidates quantitatively. Model calibration and validity check are followed to ensure good model predictability. The proposed method is demonstrated with the TFT-LCD fracture of a smartphone.
Language
English
URI
https://hdl.handle.net/10371/123695
Files in This Item:
Appears in Collections:

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share