## 1 Introduction  
The use of short fiber-reinforced composites (SFRCs) has increased significantly over the years. Due to the specific strength and stiffness in comparison to conventional polymers and the applicability for mold injection production processes, they are of special interest, e.g., in the automotive industry [1]. However, the mold injection process, as well as the finite length of the reinforcing elements result in a microstructure having a probabilistic nature, which significantly affects the mechanical properties [2] and, thus, the structural response. Following this, a probabilistic modeling approach appears to be promising to exploit the lightweight potential in the best possible way. To reduce the computational costs, the main challenge here is the incorporation of microstructural information without explicit modeling. One stochastic technique to model spatial data is random fields [3; 4; 5], which are also used in the context of material modeling. [PERSON] and [PERSON] developed a comprehensive framework starting with non-Gaussian positive-definite matrix-valued random fields [6] and tensor-valued random fields for a meso-scale stochastic model of anisotropic elastic microstructures [7]. Based on this paper by [PERSON], the approach was extended by [PERSON] et al., covering the stochastic fluctuations in fiber-reinforced composites on the mesoscale [8; 9], random interphases from atomistic simulations of polymer nanocomposites [10], and a multiscale approach for heterogeneous materials with non-Gaussian random fields [11]. Beside this, random fields are widely used, e.g., for geosystems [12], thin-walled composite cylinders [13], three-dimensional concrete microstructures [14], and the representation of the continuous mode conversion observed by the propagation of guided ultrasonic waves in thin-walled structures made of fiber-reinforced composite [15]. A first application of random fields in the context of nonlinear behavior for isotropic material was provided by [PERSON] et al. [16]. The application to SFRCs has been limited to the linear elastic domain so far [2]. However, SFRCs show prominent nonlinear behavior, because the matrix material enters the plastic domain even at operation loads [17]. Hence, to predict the structural response correctly even at low stress levels, information about the plastic deformation must be considered. Since the plastic deformation is localized initially, the required nonlinear modeling approach needs to include information about the probabilistic nature of the microstructure causing spatially distributed material properties [18]. Beside the research on random fields for probabilistic material modeling, the modeling of SFRCs at the different scales mostly focuses on homogenization approaches and does not consider the spatial distribution of the material properties on the component level. The determination of a representative volume element (RVE) for random composites was discussed by [PERSON] et al. [19] by combining the extended finite element method with a Monte Carlo sampling. [PERSON] et al. [20; 21] applied the RVE to SFRCs including artificial neural networks. [PERSON] et al. investigated the strain rate dependence of SFRCs based on RVEs [22], and [PERSON] et al. applied the RVE concept to cyclic mechanical and thermal loading [23]. Hence, the main objective of this research was the incorporation of the probabilistic material characteristics of SFRCs into a modeling approach covering the elastic and plastic domain on the component level. The presented paper is linked to a correlation structure study of the elastic-ideal plastic material behavior [24], where the moving window method [19; 25; 26] was utilized for the elastic domain and a homogenization method was used to derive the local apparent plastic material parameters [27; 28], respectively. Here, the obtained results are now employed for the numerical simulation of SFRCs on the component level, including an experimental validation.