This work introduces a comprehensive validation framework tailored for the buckling analysis of composite stiffened panels in the aerospace industry. While the certification of aeronautical structures traditionally relies on the physical ”building block approach”, manufacturers aim to mitigate temporal and financial constraints by extending the usage of numerical models. From a regulatory standpoint, this requires a rigorous definition of the model’s Domain of Validation (DoV) and a quantification of the risk associated with using the model in the extended Domain of Application. This challenge is compounded in composite structures, where the high-dimensional input space makes exhaustive testing intractable. We propose a methodology to optimize the coverage of the validation domain using sparse highfidelity experimental data guided by abundant low-fidelity simulations. Applied to the local skin buckling of Ω-stiffened panels, the approach tackles the high dimensionality via physics-informed reduction. The governing partial differential equations are non-dimensionalized to isolate the complex laminate behavior into latent parameters representing aspect ratio, orthotropy, and flexural anisotropy. A major hurdle in validating against sparse physical tests is that experimental data are not deterministic; they carry significant epistemic and aleatory uncertainties (e.g., coupon-level scatter). We address this by constructing a multi-fidelity kriging emulator that fuses this uncertain high-fidelity data with deterministic low-fidelity predictions from a Rayleigh-Ritz ”superstringer” model. The framework propagates input uncertainties through both sources to generate comparative cumulative distribution functions, using the area validation metric to quantify discrepancies. By co-kriging experimental data with physics-based trends, the emulator provides a robust trend for extrapolation outside the training set. The final paper will present the application of this framework on this use case, demonstrating how the DoV can be rigorously delineated to support virtual testing strategies.