Uncertainty management for the stochastic response of uncertain structures

P. Song, X. Q. Wang, M. P. Mignolet

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The present investigation hinges on the perspective that using the most detailed computational models (e.g., very refined meshes) and/or the most complete physical models to evaluate each sample of the response is not an efficient use of computational or time resources in the presence of aleatoric uncertainty. Rather, the fidelity of the models can be degraded as long as the induced epistemic uncertainty remains small in comparison of the aleatoric uncertainty present. This perspective is here referred to as uncertainty management and the focus of the present effort is to validate this concept to two very different structures: the first is linear modeled in finite elements while the second behaves nonlinearly, is part of a multiphysics problem and is represented as a reduced order model (ROM). The reduction of fidelity and increase in computational speed is achieved in the first problem by relying on a coarse model while in the second sparsity is introduced in a large group of ROM coefficients. For these model downgrades which induce only small changes in the response, it is indeed shown that a well identified/calibrated lower fidelity model provides indeed a close fit of the random response of the higher order one. The maximum entropy nonparametric approach to uncertainty modeling is a convenient framework for this uncertainty management strategy given its capability to represent both aleatoric and some epistemic uncertainties.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF


ConferenceAIAA Scitech Forum, 2020
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering


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