Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review.

Herein, state-of-the-art multiscale modeling methods have been described. This research includes notable molecular, micro-, meso-, and macroscale models for hard (polymer, metal, yarn, fiber, fiber-reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite-element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity-driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state-of-the-art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data-driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in-depth discussion on data-driven models would provide researchers with the necessary tools to build real-time composite structure monitoring and lifecycle prediction models.