Feasible Probabilistic Learning Method for Model-Form Uncertainty Quantification in Vibration Analysis

What is it about?

A probabilistic method is presented for modeling and quantifying model-form uncertainties in computational models for vibration analysis. The method can be interpreted as a learning approach as it extracts information and/or knowledge from data and infuses it in a computational model. It is demonstrated for vibration analyses associated with the shape design of a supersonic engine nozzle.

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