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With the continuous advancement of technologies in data acquisition, deep learning, and model generation, data−driven methods have provided a powerful tool for predicting the properties of fiber−reinforced composites, leveraging their unique advantages in uncovering high−dimensional nonlinear relationships, constructing surrogate models, and processing multimodal data. This review systematically reviews recent progress in this field, categorizing digital characterization methods into four types: collection of intrinsic material parameters, image−driven feature extraction, physics−informed feature engineering, and cross−scale data−driven techniques. It summarizes the modeling strategies and prediction accuracy of data−driven models in predicting the mechanical, thermal, acoustic, and electrical properties of composites. The engineering significance of interpretability analysis and uncertainty quantification techniques is elaborated, highlighting their roles in enhancing model transparency and quantifying prediction risks. This review aims to provide a comprehensive perspective—from theoretical foundations to engineering applications—for the deeper application of data−driven methods in predicting the properties of composites.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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