W. Pengyu, A. Mosavi, I. Felde, M. Azodinia, A. Delavar, A. Azamat, S. Wei: Machine Learning for Modeling Stress Evolution. ACTA POLYTECHNICA HUNGARICA Vol. 22, No. 12, 2025. pp. 95–114. ISSN 1785-8860 link

Abstract: We present a detailed review and evaluation of machine learning (ML) methods for modeling and predicting stress evolution in various materials and systems. Stress evolution is considered a fundamental phenomenon in materials science, structural engineering and biomechanics. It is frequently modeled with deterministic methods, which struggle to handle high-dimensional, complex and non-linear data. A promising substitute is Machine Learning (ML), which offers instruments to enhance predictive accuracy and more effectively capture complex patterns. We used the Scopus database to find relevant literature and the PRISMA framework for systematic screening for creating an extensive database for this review. Based on how well supervised, unsupervised and deep learning approaches apply to stress modeling, under various loading and environmental circumstances, we present a new taxonomy of machine learning approaches. Furthermore, we critically evaluate these approaches’ advantages and disadvantages, and further highlight the significance of feature engineering, data quality and model interpretability. The review ends by outlining potential future directions, especially with regard to deep and hybrid models that combine ML with traditional techniques to improve prediction of stress evolution in a variety of applications.