From Simulation to Validation in Ensuring Quality and Reliability in Model-Based Predictive Analysis

Abstract

The increasing complexity of artificial intelligence and machine learning models has raised concerns about balancing model accuracy and interpretability. While advanced software tools facilitate model design, they also introduce challenges in selecting models that offer both high quality and manageable complexity. Validation techniques such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the Akaike Information Criterion (AIC) enable quantitative assessment, but empirical studies indicate that higher complexity does not always improve predictive performance. This study proposes an approach to evaluate model complexity versus accuracy in predicting the absorption properties of composite materials with varying textile fibre content (10%, 20%, 30%, 40%). Using MATLAB’s Curve Fitting Toolbox, we assessed polynomial, Fourier, and Gaussian regression models. The Gaussian regression model with six parameters (Gauss6) achieved the best balance between complexity and accuracy (R2 = 0.9429; RMSE = 0.013537; MAE = 0.004885). Increasing parameters beyond six showed diminishing returns, as confirmed by AIC (−2806.93 for Gauss6 vs. −2847.17 for Gauss7). These findings emphasise that higher model complexity does not necessarily enhance quality, highlighting the importance of structured model validation. This study provides insights for optimising predictive modelling in material science and other domains.