Due to the interlocking of asperities, rubber friction is a dissipative process that converts mechanical energy into heat and produces material wear. Furthermore, wear is the loss of material under the frictional sliding of two bodies against each other. The wear parameters calibrated by using Linear Friction Tester (LFT) experiments lead to an overestimation of wear when used in Laboratory Abrasion & Skid Tester 100 (LAT100) simulations. Furthermore, calibration of wear parameters by LAT100 experiment uses assumptions regarding the contact area and slip velocity which cannot be measured directly. Therefore, numerical calibration can be one way to obtain reliable wear parameters. To numerically calibrate the wear parameters, a hybrid meta-finite element method (FEM) approach is developed. For the FEM representation of the LAT100 experiment, a steady state rolling framework is adopted and an arbitrary LAGRANGIAN-EULERIAN(ALE) adaptive meshing procedure is used for wear calculation. Due to the mesh dependency of the ALE adaptive meshing, a failure surface algorithm is developed. The workflow is composed of a loop of three main steps. First, an initial set of wear parameters is identified by the minimization algorithm. Next, the failure surface algorithm decides if these wear parameters are passed to the FEM or the meta-model. Finally, the FEM or the meta-model predicts the wear mass, and a new iteration is started from step one. The loop ends once optimal wear parameters are found. Both approaches are able to predict the amount of wear of the LAT100 with a similar accuracy.