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pro vyhledávání: '"Mike McKerns"'
We demonstrate that the recently developed Optimal Uncertainty Quantification (OUQ) theory, combined with recent software enabling fast global solutions of constrained non-convex optimization problems, provides a methodology for rigorous model certif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ffa1a613cfb1d73ccd56e61f3654c3e2
https://doi.org/10.1142/9789811204579_0014
https://doi.org/10.1142/9789811204579_0014
Autor:
Houman Owhadi, Bo Li, Mike McKerns, P.-H.T. Kamga, Lan Huong Nguyen, Timothy Sullivan, Michael Ortiz
Publikováno v:
Journal of the Mechanics and Physics of Solids. 72:1-19
We present an optimal uncertainty quantification (OUQ) protocol for systems that are characterized by an existing physics-based model and for which only legacy data is available, i.e., no additional experimental testing of the system is possible. Spe
Publikováno v:
ESAIM: Mathematical Modelling and Numerical Analysis. 47:1657-1689
We consider the problem of providing optimal uncertainty quantification (UQ) --- and hence rigorous certification --- for partially-observed functions. We present a UQ framework within which the observations may be small or large in number, and need
Autor:
Houman Owhadi, Bo Li, G. Ravichandran, Addis Kidane, Michael Ortiz, Mike McKerns, A. Lashgari, Timothy Sullivan, Mark A. Stalzer
Publikováno v:
Journal of the Mechanics and Physics of Solids. 60:983-1001
This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quantification (UQ) protocol based on concentration-of-measure inequalities. Specific aims are to establish the feasibility of the protocol and its basic p
Publikováno v:
International Journal for Numerical Methods in Engineering. 85:1499-1521
We consider uncertainty quantification in the context of certification, i.e. showing that the probability of some ‘failure’ event is acceptably small. In this paper, we derive a new method for rigorous uncertainty quantification and conservative
Publikováno v:
Journal of Medical Devices. 7
We discuss recent mathematical and computational results on uncertainty quantification (UQ) in the presence of uncertainty about the correct probabilistic and physical models. Such UQ problems can be formulated as constrained optimization problems wi
We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5018754f38c9d8d6a0001974b585ba7f
Publikováno v:
Procedia - Social and Behavioral Sciences. (6):7751-7752
In [LOO08], it was proposed that a concentration-of-measure inequality known as Mc-Diarmid’s inequality [McD89] be used to provide upper bounds on the failure probability of a system of interest, the response of which depends on a collection of ind