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The research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components. In recent years, machine learning, especially neural networks, has been widely applied in fatigue life prediction. However, the scarcity of fatigue data has limited the further application of neural networks in fatigue prediction. To address this issue, physics-informed neural networks that consider prior physical knowledge of fatigue have gradually gained attention. Firstly, provided an overview of the classification of machine learning algorithms and the application of neural-network models in multiaxial fatigue life prediction. Then, it focused on a deep exploration of the research on material fatigue life prediction based on physics-informed neural networks. Finally, the development of physics-informed neural networks was introduced from three aspects: physics-informed input features, the construction of physics-informed loss functions, and physics-informed network frameworks. Relevant studies show that physics-informed neural networks can exhibit better physical consistency and prediction performance in the process of multiaxial fatigue life prediction of materials.
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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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