To fully realize the potential of Digital Twins (DTs), it requires Verification, Validation, and Uncertainty Quantification (VVUQ) to be a continual process that must adapt to changes in the physical counterpart, digital twin virtual models, data, and the prediction/decision task at hand. Yet, despite rapid advances in DT technologies across engineering disciplines, the field lacks standardized numerical benchmarks for systematically verifying and comparing system identification methods. This gap limits the reproducibility of results and slows methodological progress. In this contribution, we present a new suite of numerical benchmarks designed for damage localization in structural systems. The benchmarks are based on simple two-dimensional geometries to ensure accessibility and reproducibility, while exhibiting non-trivial mechanical behavior representative of realistic structures. Multiple damage scenarios are included, varying in location, extent, and severity, enabling comprehensive testing of algorithm robustness and accuracy. For each benchmark case, we provide complete problem definitions, including geometry, material parameters, boundary conditions, loading configurations, and high-fidelity finite element reference solutions. These datasets allow users to validate their forward models prior to applying identification techniques and to directly compare results across different numerical approaches. We further introduce a general optimization-based identification framework that minimizes discrepancies between simulated and reference responses, supported by adjoint-based sensitivity analysis and regularization strategies for stable parameter reconstruction [1]. In our talk, we will demonstrate this reference method together with the reference solutions obtained for the proposed benchmarks. We will also present our software solution based on open research software KratosMultiphysiscs [2], which can serve as a starting point for further methodological developments and comparative studies.