System Identification via Validation and Adaptation for Model Updating Applied to a Nonlinear Cantilever Beam

TL;DR

This paper presents an advanced neural network-based system identification method called SIVA, which accurately models complex nonlinear systems like a cantilever beam with nonlinear attachments, using validation and adaptation techniques.

Contribution

The work extends SIVA to nonlinear systems, demonstrating its effectiveness in accurately estimating parameters and updating models for complex, highly nonlinear structures.

Findings

Accurate parameter estimation for nonlinear cantilever beam

Effective model updating for complex nonlinear systems

Demonstrated robustness on simulated vibration data

Abstract

The recently proposed System Identification via Validation and Adaptation (SIVA) method allows system identification, uncertainty quantification, and model validation directly from data. Inspired by generative modeling, SIVA employs a neural network that converts random noise to physically meaningful parameters. The known equation of motion utilizes these parameters to generate fake accelerations, which are compared to real training data using a mean square error loss. For concurrent parameter validation, independent datasets are passed through the model, and the resulting signals are classified as real or fake by a discriminator network, which guides the parameter-generator network. In this work, we apply SIVA to simulated vibration data from a cantilever beam that contains a lumped mass and a nonlinear end attachment, demonstrating accurate parameter estimation and model updating on complex, highly nonlinear systems.