Determining whether the selected model is valid or not is part of the overall model development process [3]. Regression models are a fundamental tool for data analysis, allowing the values of a dependent variable to be predicted from a set of independent variables. One of the most widely used approaches to building regression models is based on Gaussian processes (GPs). The applications of GP-based regression are broad and include areas such as machine learning and neural networks [4], optimisation, material selection [5], time series prediction, education [6], and many others. GP models are particularly valuable in situations involving small- to medium-sized datasets, where their ability to represent uncertainty provides a competitive advantage over traditional regression methods. However, as with any analytical technique, simulation models require thorough evaluation to ensure their reliability, accuracy and applicability in practice. In the literature [7], the authors analysed the use of metrics in 106 papers. The following figure (Figure 1) shows a summary of the metrics used in the reviewed studies [7]. The focus of this paper is to describe the steps involved in the design of a simulation model and its evaluation. This description is illustrated by an example using measured absorption data in composite materials containing textile fibres. Various software applications provide model design tools that make it easier to find an optimal model. However, the quality of the model is almost always evaluated only on the basis of quantitative indicators (R-squared, RSME, MAE). In this paper, we will focus on describing the steps of designing a simulation model and evaluating it not only in terms of quality but also in terms of complexity. This description will be given by means of an illustrative example using measured absorption data in composite materials containing textile fibres. The contribution of this paper is to point out that an indiscriminate increase in the complexity of the model (adding parameters) will not significantly improve the quality of the model. For model evaluation, it is necessary to use criteria that deal with the relationship between quality and complexity. Figure 1: Overview of metrics usage based on a previous study [7]. Related Work  
The comparison of the impact of complexity vs. model quality can be found in several literature sources. Given the less powerful application programmes of the past, it was generally preferred to design a less complex model. The findings were based on the fact that simpler models offer many advantages, while the accuracy of the model is only marginally reduced, as long as the simplification is not taken too far [8]. The issue of complexity and model quality is discussed in more detail in [9]. The authors examine the validity of previous claims that accuracy improves with decreasing returns as complexity increases, up to the point where measurement errors begin to dominate. The verification is carried out by examining the effect of successive simplifications with models of three different systems: a manufacturing model, a service model and a case study model. The conclusion was that the actual relationship between accuracy and complexity depends on the nature of the system, the model that the system represents, and the modeller's choice of simplifications, including the order in which the simplifications are introduced. Although increased complexity often leads to increased accuracy, this is not always the case. Results supporting the previous statement are also found in the work of the authors [10], who investigate this relationship in the field of energy systems. They point to the need to maintain a balance between the complexity and quality of models. In the context of modelling dynamic systems, this relationship is explored in previous work [11]. The article examines the trade-off between accuracy and complexity in solving the problem of constructing mathematical models of dynamic systems.