Composite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing history. This intrinsic complexity makes predictive modeling of composites expensive, because repeated experiments and high-fidelity simulations are needed to cover large design spaces of material, structure, and manufacturing. Multi-fidelity surrogate modeling addresses this challenge by combining abundant, less expensive data with limited high-accuracy data to recover reliable high-fidelity predictions. This review presents a structured overview of multi-fidelity modeling for composite mechanics, covering Gaussian-process or Kriging-based methods, including co-Kriging, coregionalization models, autoregressive formulations, nonlinear autoregressive Gaussian processes, multi-fidelity deep Gaussian processes, and multi-fidelity neural networks. Their distinctions are examined in terms of cross-fidelity correlation, discrepancy representation, uncertainty quantification, and scalability. Selected examples of their applications to composites are introduced according to the roles that multi-fidelity surrogates play in engineering problems, including forward prediction for rapid exploration of material design spaces, inverse optimization for composite parameter identification and design search under limited high-fidelity access, and workflow integration, where heterogeneous data sources, constraints, and validation requirements determine model utility. Open question discussions highlight recurring challenges specific to composites, such as regime-dependent fidelity gaps associated with nonlinear damage and manufacturing history, mismatches between simulations and experiments, and uncertainty propagation across multi-fidelity models.

翻译：复合材料具有强烈的层次性和各向异性特性，其由跨越组分、铺层、层合板、结构及制造历史的耦合机制驱动。这种内在复杂性使得复合材料的预测建模成本高昂，因为需要重复实验和高保真模拟来覆盖材料、结构和制造的大设计空间。多保真代理建模通过将丰富但成本较低的数据与有限的高精度数据相结合，以恢复可靠的高保真预测，从而解决了这一挑战。本综述对复合材料力学的多保真建模进行了结构化概述，涵盖了基于高斯过程或克里金的方法，包括协同克里金、协同区域化模型、自回归公式、非线性自回归高斯过程、多保真深度高斯过程以及多保真神经网络。从交叉保真相关性、差异表示、不确定性量化和可扩展性方面考察了它们的区别。根据多保真代理在工程问题中的作用，介绍了它们应用于复合材料的选定示例，包括用于快速探索材料设计空间的正向预测、在高保真访问受限情况下进行复合材料参数识别和设计搜索的逆向优化，以及工作流集成——其中异构数据源、约束和验证要求决定了模型的实用性。开放性问题的讨论强调了复合材料特有的反复出现的挑战，例如与非线性损伤和制造历史相关的工况依赖性保真度差距、模拟与实验之间的不匹配，以及跨多保真模型的不确定性传播。