## 8 Conclusions  
Future research on composite creep behavior is heading in the direction of a more precise, effective, and multiscale understanding, which is made possible in large part by sophisticated modeling tools, cleverer data integration, and innovative material systems. Key research trends and future directions are broken down as follows:  
Development of multiscale and multi-physics modeling: Researchers have simulated creep from the fiber-matrix level up to the whole structure by merging microstructural, mesoscale, and macroscale models. This aids in capturing the effects of voids, fiber-matrix interactions, and other microstructural elements on long-term deformation. A new field of research is the integration of molecular dynamics (MD), micromechanics, and finite element (FE) methods for holistic creep simulations.  
Creep behavior was already predicted from simulation or experimental data using machine learning and artificial intelligence. In order to maintain the physical plausibility of predictions, hybrid models can integrate ML with physics-based modeling (also known as \"physics-informed ML\"). To support the design and life prediction of composite structures in service, real-time predictive systems using AI can be built.  
Digital twins and accelerated testing can help optimize creep testing procedures while cutting expenses and time. Using real-time data input, virtual models of composite parts may continuously replicate creep behavior. Digital twins can be used in the automotive and aerospace industries to track and forecast damage caused by creep in real time.  
Growing interest in bioinspired materials and 3D woven, braided, or architected composites raises the need for new creep theories or AI-assisted models because these materials typically have distinct creep reactions that are difficult for conventional models to anticipate.  
Probabilistic models are driven by variations in manufacturing flaws, service conditions, and material qualities. To increase the reliability of predictions and account for uncertainties, they must make use of probabilistic machine learning models and Bayesian techniques.  
Microcracking, delamination, and fiber breaking necessitate damage-coupled creep models (real-world creep is not just a gradual deformation--it is associated with other factors). Current applications typically overlook progressive degradation.  
Developing long-lasting composites requires an understanding of how environmental influences affect creep behavior. In order to forecast material performance under real-world circumstances, recent research has concentrated on modeling the combined effects of temperature, moisture, and mechanical loading on creep.  
The industry demands recyclable composites and sustainability. It is necessary to understand and model the long-term creep behavior of green materials, such as biobased matrices and natural fibers, as they become more prevalent.  
Conceptualization, M.K. and S.V.; methodology, M.K. and S.V.; software, M.K. and S.V.; validation, M.K. and S.V.; formal analysis, M.K. and S.V.; investigation, M.K. and S.V.; resources M.K. and S.V.; data curation, M.K. and S.V.; writing--original draft preparation, M.K. and S.V.; writing--review and editing, M.K. and S.V.; visualization, M.K. and S.V.; supervision, M.K. and S.V.; project administration, M.K. and S.V.; funding acquisition, M.K. and S.V. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by Transilvania University of Brasov, HBS 2671/2020.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.