Partitioned analysis of coupled numerical models considering imprecise parameters and inexact models

Ismail Farajpour, Sez Atamturktur

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The present study develops an integrated coupling and uncertainty quantification framework for strongly coupled models that explicitly considers the propagation of uncertainty and bias inherent in model prediction between constituents during the iterative coupling process. Utilizing optimization techniques, three distinct configurations are formulated that differ in sequence of coupling and uncertainty quantification campaigns. Focusing on a controlled structural dynamics problem, the systematic biases from the constituents are quantified, from which the critical components of the model that require further improvement can be identified to aid in the prioritization of future code development efforts.

Original languageEnglish (US)
Pages (from-to)145-155
Number of pages11
JournalJournal of Computing in Civil Engineering
Volume28
Issue number1
DOIs
StatePublished - Jan 2014

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Partitioned analysis of coupled numerical models considering imprecise parameters and inexact models'. Together they form a unique fingerprint.

Cite this