Abstract A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by the dominant influence of a small number of vortex structures, but the complexity of these flows makes it difficult to conduct a quantitative analysis of this influence at the level of individual vortices. The method presented here combines machine learning-inspired clustering methods with a rigorous mathematical partitioning of aerodynamic loads to enable detailed quantitative analysis of vortex kinematics and vortex-induced aerodynamic loads. We demonstrate the utility of this approach by applying it to an ensemble of 165 distinct Navier-Stokes simulations of flow past a sinusoidally pitching airfoil.Insights enabled by the current methodology include the identification of a period-doubling route to chaos in this flow,and the precise quantification of the role that leading-edge vortices play in driving aeroelastic pitch oscillations.Keywords:Fluid-structure interaction, Pitching airfoils, Machine learning, Data-driven methods, Vortex dynamics