Structural System Identification via Validation and Adaptation

2025/06/25 by López, Cristian, Moore, Keegan J.
#Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2506.20799

Abstract

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.

Citations

• Energy-based dual-phase dynamics identification of clearance nonlinearities
• A Data-Driven, Energy-based Approach for Identifying Equations of Motion in Vibrating Structures Directly from Measurements
• A survey on text generation using generative adversarial networks
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• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
• Wasserstein GAN
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• Constrained Sparse Galerkin Regression
• SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Related

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• Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural Networks
• Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
• Parameter estimation of structural dynamics with neural operators enabled surrogate modeling