用于结构识别的带自适应基函数精化的降阶物理信息神经网络
Reduced-Order Physics-Informed Neural Network with Adaptive Basis Refinement for Structural Identification
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中文总结 AI 辅助
本研究提出带自适应基函数精化的降阶物理信息神经网络（RO-PINN）框架，用于结构识别，可在物理已知或不完整时，以更低成本实现准确的参数与残余恢复力联合识别。
中文摘要 AI 辅助
物理信息神经网络（PINNs）为求解正问题和反问题提供了灵活框架，但其直接应用于结构动力学仍受限于高系统维度和因物理不完整产生的模型形式误差。降阶模型（ROMs）可缓解维度瓶颈，但现有PINN-ROM耦合通常依赖固定降阶子空间、针对正模拟或假设物理完整，限制了其在参数变异性或系统知识不完整下的反问题识别应用。为解决这些局限，本研究提出带自适应基函数精化的降阶物理信息神经网络（RO-PINN）框架，用于物理已知和不完整情况下的结构识别。通过投影，降阶控制方程被直接嵌入PINN损失，便于在低维潜在空间中学习。自适应方案在训练过程中更新投影基函数，使潜在空间逐步随结构参数变化或学习到的残余恢复力重新对齐，这种重新对齐减少了基函数失配误差并限制其对推断残余力的影响。该方法在带非线性滞回支撑的四层钢框架上进行验证，采用稀疏且含噪声的测量数据。结果显示，在考虑的案例中，参数识别与贝叶斯模型更新相当或更准确且计算成本更低，可在物理不完整情况下恢复未建模的非线性恢复力，且在同一框架内联合识别残余恢复力和结构参数。总体而言，RO-PINN通过在单一公式中整合降阶建模、自适应基函数精化和物理信息学习，为结构识别提供了统一框架。
英文摘要
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems. However, their direct application to structural dynamics remains limited by high system dimensionality and model-form errors arising from incomplete physics. Reduced-order models (ROMs) can alleviate the dimensionality bottleneck, yet existing PINN-ROM couplings typically rely on fixed reduced subspaces, target forward simulations, or assume complete physics, restricting their use for inverse identification under parametric variability or incomplete system knowledge. To address these limitations, this work proposes a Reduced-Order Physics-Informed Neural Network (RO-PINN) framework with adaptive basis refinement for structural identification under known and incomplete physics. Via projection, reduced governing equations are embedded directly into the PINN loss, facilitating learning in a low-dimensional latent space. An adaptive scheme updates the projection basis during training so that the latent space is progressively realigned with evolving structural parameters or learned residual restoring forces. This realignment reduces basis-mismatch errors and limits their influence on the inferred residual force. The method is validated on a four-story steel frame with nonlinear hysteretic braces under sparse and noisy measurements. Results show parameter identification comparable to or more accurate than Bayesian model updating with lower computational cost in the considered cases, recovery of unmodeled nonlinear restoring forces under incomplete physics, and joint identification of residual restoring forces and structural parameters within the same framework. Overall, RO-PINN provides a unified framework for structural identification by integrating reduced-order modeling, adaptive basis refinement, and physics-informed learning within a single formulation.
