Uncertainty Quantification in Aeroelasticity: Recent Results and Research Challenges

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Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “, climate modeling [74], fluid flow [65], systems engineering [217], among many others).”
Modeling physical uncertainties in dynamic stall induced fluid-structure interaction of turbine blades using arbitrary polynomial chaos (2007, background): “Pettit [35,36] has outlined the influence of uncertainty in various aspects of aeroelastic design, both static and dynamic which are integral parts of an airframe design.”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “Uncertainty quantification (UQ) is of great importance in many areas of computational science (e.g., climate modeling [64], fluid flow [55], systems engineering [195], among many others).”
Design of Composite Wings Including Uncertainties : A Probabilistic Approach (2009, background): “Some work has been undertaken on the influence of uncertainties on aeroelastic behavior [17–19].”
Reliability-Based Design Optimization of Nonlinear Aeroelasticity Problems (2010, background): “Such nonlinearities are found in high-aspect-ratio wings, such as those found in high-altitude surveillance airplanes, and are characterized by a high flexibility and large deformations [4].”