从数据中直接学习结构系统参数并验证模型准确性。
Structural System Identification via Validation and Adaptation
- 用神经网络将噪声映射为物理参数，生成虚拟加速度。
- 通过真实与生成加速度的误差最小化，实现参数估计。
- 引入判别器验证生成数据真实性，提升模型可靠性。
估计控制方程的参数值对于融合实验数据与科学理论、理解、验证和预测复杂系统动态至关重要。本文提出一种新的结构系统识别（SI）方法，可直接从数据中进行参数估计、不确定性量化与模型验证。受生成建模框架启发，神经网络将随机噪声映射为具有物理意义的参数，这些参数用于已知运动方程以生成虚假加速度，并通过均方误差损失与真实训练数据对比。为同时验证学习到的参数，我们使用独立的验证数据集。这些数据集生成的加速度由判别网络评估，判断输出是真实还是伪造，从而指导参数生成网络优化。分析与实际实验表明，该方法在多种非线性结构系统上均实现了高精度的参数估计与模型验证。
原文摘要 · Abstract (English)
Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equation of motion to obtain fake accelerations, which are compared to real training data via a mean square error loss. To simultaneously validate the learned parameters, we use independent validation datasets. The generated accelerations from these datasets are evaluated by a discriminator network, which determines whether the output is real or fake, and guides the parameter-generator network. Analytical and real experiments show the parameter estimation accuracy and model validation for different nonlinear structural systems.
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