Accurate prediction of structural dynamics is imperative for preserving digital twin fidelity throughout operational lifetimes. Parametric models with fixed nominal parameters often omit critical physical effects due to simplifications in geometry, material behavior, damping, or boundary conditions, resulting in model form errors (MFEs) that impair predictive accuracy. This work introduces a comprehensive framework for MFE estimation and correction in high-dimensional finite element (FE) based structural dynamical systems. The Gaussian Process Latent Force Model (GPLFM) represents discrepancies non-parametrically in the reduced modal domain, allowing a flexible data-driven characterization of unmodeled dynamics. A linear Bayesian filtering approach jointly estimates system states and discrepancies, incorporating epistemic and aleatoric uncertainties. To ensure computational tractability, the FE system is projected onto a reduced modal basis, and a mesh-invariant neural network maps modal states to discrepancy estimates, permitting model rectification across different FE discretizations without retraining. Validation is undertaken across five MFE scenarios-including incorrect beam theory, damping misspecification, misspecified boundary condition, unmodeled material nonlinearity, and local damage demonstrating the surrogate model's substantial reduction of displacement and rotation prediction errors under unseen excitations. The proposed methodology offers a potential means to uphold digital twin accuracy amid inherent modeling uncertainties.

翻译：结构动力学的精确预测对于维持数字孪生体在整个运行周期内的保真度至关重要。采用固定名义参数的参数化模型常因几何简化、材料行为、阻尼或边界条件的简化而忽略关键物理效应，导致模型形式误差（MFE），从而损害预测精度。本研究提出一个用于高维有限元（FE）结构动力系统中MFE估计与校正的综合框架。高斯过程隐力模型（GPLFM）在降阶模态域中以非参数化方式表征系统差异，实现对未建模动力学的灵活数据驱动描述。线性贝叶斯滤波方法联合估计系统状态与差异量，同时纳入认知不确定性与随机不确定性。为确保计算可行性，将有限元系统投影至降阶模态基，并采用网格无关的神经网络将模态状态映射至差异估计值，从而实现在不同有限元离散化方案间的模型校正而无需重新训练。通过在五种MFE场景（包括错误梁理论、阻尼误设、边界条件误设、未建模材料非线性及局部损伤）下的验证表明，该代理模型在未见激励下能显著降低位移与转动预测误差。所提方法为在固有建模不确定性下维持数字孪生精度提供了潜在途径。