Fabricated wind turbine blades have unavoidable deviations from their designs due to imperfections of the manufacturing processes. Model updating is a common approach to enhance model predictions and therefore improve the numerical blade design accuracy compared to the built blade. An updated model can provide a basis for a digital twin of the rotor blade including the manufacturing deviations. State of the art in structural model updating are classical optimization algorithms most often combined with reduced order or surrogate models. However, these deterministic methods suffer from high computational costs and a missing probabilistic evaluation.This study approaches the model updating task by inverting the model through the application of Invertible Neural Networks, which allow for inferring a posterior distribution of the input parameters from given output parameters, without costly optimization or sampling algorithms. In our use case, rotor blade cross sections are updated to match given cross sectional parameters. To this end, a sensitivity analysis of the input and output parameters first selects relevant features in advance to then set up and train the Invertible Neural Network.The trained network predicts with outstanding accuracy most of the cross sectional input parameters for different radial positions, i.e. the posterior distribution of the features show a narrow width. At the same time, it identifies some parameters that are hard to recover accurately or contain intrinsic ambiguities.Hence, we demonstrate that invertible neural networks are highly capable for structural model updating.