标题： 基于物理的复合板冲击定位与力估计及不确定性量化 显示英文标题

标题： Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification

摘要： 物理引导的方法为在复合结构中准确且具有泛化能力的冲击识别提供了一条有前景的路径，特别是在实验数据稀疏的情况下。 本文提出了一种混合框架，用于复合板的冲击定位和力估计，结合了初阶剪切变形理论（FSDT）的数据驱动实现与机器学习和不确定性量化。 结构配置和材料特性从色散关系中推断出来，而边界条件则通过模态特征进行识别，以构建一个低保真但物理一致的FSDT模型。 该模型通过监督学习实现了物理信息的数据增强，用于外推定位。 同时，从同一模型中派生出的自适应正则化方案提高了冲击力重建的鲁棒性。 该框架通过将定位不确定性传播到力估计过程中来考虑不确定性，产生概率输出。 在复合板实验中的验证确认了该框架在减少对大型训练数据集依赖方面的准确性、鲁棒性和效率。 所提出的方法为复合航空结构的冲击监测和结构健康管理提供了一个可扩展和可转移的解决方案。 显示英文摘要

摘要： Physics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This paper presents a hybrid framework for impact localisation and force estimation in composite plates, combining a data-driven implementation of First-Order Shear Deformation Theory (FSDT) with machine learning and uncertainty quantification. The structural configuration and material properties are inferred from dispersion relations, while boundary conditions are identified via modal characteristics to construct a low-fidelity but physically consistent FSDT model. This model enables physics-informed data augmentation for extrapolative localisation using supervised learning. Simultaneously, an adaptive regularisation scheme derived from the same model improves the robustness of impact force reconstruction. The framework also accounts for uncertainty by propagating localisation uncertainty through the force estimation process, producing probabilistic outputs. Validation on composite plate experiments confirms the framework's accuracy, robustness, and efficiency in reducing dependence on large training datasets. The proposed method offers a scalable and transferable solution for impact monitoring and structural health management in composite aerostructures.