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This presentation will focus on concepts pertaining to sensitivity analysis (SA) and uncertainty quantification (UQ) for smart materials and adaptive structures. Pertinent issues are first illustrated in the context of applications utilizing piezoelectric and shape memory alloy actuators, finite-deformation viscoelastic models, x-ray crystallography, and quantum-informed continuum models. This will demonstrate that the basic UQ goal is to ascertain uncertainties inherent to parameters, initial and boundary conditions, experimental data, and models themselves to make predictions with improved and quantified accuracy. The use of data, to improve the predictive accuracy of models, is central to uncertainty quantification so it is natural to next provide an overview of how Bayesian techniques can be used to construct distributions for model inputs. The discussion will subsequently focus on computational techniques to propagate these distributions through complex models to construct prediction intervals for statistical quantities of interest such as expected stresses in viscoelastic materials, displacements in macro-fiber composites, and strains in SMA tendons. The use of sensitivity analysis to isolate critical model inputs and reduce model complexity is synergistic with uncertainty quantification and will be discussed next. The presentation will conclude with discussion detailing how uncertainty quantification can be used to improve robust control designs for smart material systems.
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