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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. [ABSTRACT FROM AUTHOR]

The electrochemical reduction of CO2 provides a pathway to a sustainable carbon cycle, allowing for the production of hydrocarbons critical for both chemical industry and mobility. Major engineering challenges need to be met in order to achieve efficient and large‐scale CO2 electrolysis. One central challenge is to find the optimum structure for the gas diffusion electrode at the heart of the electrolyzer. An optimum structure will achieve higher conversion efficiencies, lifetime, and product selectivity at lower cost. Critical structural properties of the electrode span the scale from nanometers to millimeters. To rationalize the optimization process, it behooves us to obtain a fully resolved multi‐scale model of the electrode. This digital twin, produced by bridging scale and employing multiple imaging methods, enables the digital study, simulation, and modification of the structure of the electrode. The model is used to simulate the transport processes vital to the functioning of the electrode thus further advancing the digital twin. Subsequently, it is explored how changes to the structure affect the predicted transport properties. The digital twin presented is only supposed to be the kernel and will be complemented by numerous future works. [ABSTRACT FROM AUTHOR]

Characterization, digital twins, machine learning, simulation Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Keywords: characterization; digital twins; machine learning; simulation EN characterization digital twins machine learning simulation 1 1 1 09/05/22 20220901 NES 220901 B Lithium Batteries b In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. [Extracted from the article]