In this work, an aeroelastic prediction framework is formulated by blending control-oriented techniques that consist of linear fractional transformation representation, identification of nonlinear operators, and stability boundary prediction for both flutter and limit-cycle-oscillation phenomena. The final product is an efficient tool devised to identify, characterize, and predict flutter/limit-cycle-oscillation conditions by taking full advantage of the information embedded in the flight data. A novel data-based amplitude- and airspeed-dependent operator is developed to consistently fit within the � -analysis framework. By exploiting the algebraic structure of the identified block-oriented models, a parameter-varying model is devised to represent its amplitude- and airspeed-dependent dynamic behavior. To illustrate this flutter/limit-cycle-oscillation prediction framework, the developed algorithms areapplied to astructurallynonlineartwo-dimensional wingsection while including uncertain stiffness parameters.