NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems

TL;DR

NeuralSI introduces a novel framework combining neural networks with PDEs for accurate nonlinear structural parameter identification from limited data, improving extrapolation and interpolation in structural health monitoring.

Contribution

The paper presents NeuralSI, a new physics-informed neural network framework that effectively estimates nonlinear structural parameters from limited measurements, outperforming existing methods.

Findings

Reduces displacement distribution errors by 2-5 orders of magnitude.

Successfully estimates unknown structural parameters from limited data.

Enhances extrapolation capabilities under extreme conditions.

Abstract

Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters. Additionally, real-world structural identification problems encounter many challenges. For example, the lack of accurate baseline models, high dimensionality, and complex multivariate partial differential equations (PDEs) pose significant difficulties in training and learning conventional data-driven algorithms. This paper explores a new framework, dubbed NeuralSI, for structural identification by augmenting PDEs that govern structural dynamics with neural networks. Our approach seeks to estimate nonlinear parameters from governing equations. We consider the vibration of nonlinear beams with two unknown parameters, one that represents geometric and material variations, and another that captures energy losses in the system mainly through damping. The data for parameter estimation is obtained from a limited set of measurements, which is conducive to applications in structural health monitoring where the exact state of an existing structure is typically unknown and only a limited amount of data samples can be collected in the field. The trained model can also be extrapolated under both standard and extreme conditions using the identified structural parameters. We compare with pure data-driven Neural Networks and other classical Physics-Informed Neural Networks (PINNs). Our approach reduces both interpolation and extrapolation errors in displacement distribution by two to five orders of magnitude over the baselines. Code is available at https://github.com/human-analysis/neural-structural-identification