Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks

TL;DR

This paper investigates the use of Physics-Informed Neural Networks (PINNs) for system identification and response estimation in dynamical systems, especially under sparse data and uncertainties, demonstrating their effectiveness across multiple applications.

Contribution

The study introduces PINNs for joint state and parameter estimation, including a Bayesian approach for uncertainty quantification, highlighting their robustness in complex, uncertain dynamical systems.

Findings

PINNs effectively estimate states with sparse sensor data.

Joint state-parameter estimation is feasible with PINNs.

Bayesian PINNs quantify uncertainties in model parameters.

Abstract

The accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration control. Often, these models originate from physics-based principles and can be derived from corresponding governing equations, often of differential equation form. However, complex system characteristics, such as nonlinearities and energy dissipation mechanisms, often imply that such models are approximative and often imprecise. This challenge is further compounded in SHM, where sensor data is often sparse, making it difficult to fully observe the system's states. To address these issues, this paper explores the use of Physics-Informed Neural Networks (PINNs), a class of physics-enhanced machine learning (PEML) techniques, for the identification and estimation of dynamical systems. PINNs offer a unique advantage by embedding known physical laws directly into the neural network's loss function, allowing for simple embedding of complex phenomena, even in the presence of uncertainties. This study specifically investigates three key applications of PINNs: state estimation in systems with sparse sensing, joint state-parameter estimation, when both system response and parameters are unknown, and parameter estimation within a Bayesian framework to quantify uncertainties. The results demonstrate that PINNs deliver an efficient tool across all aforementioned tasks, even in presence of modelling errors. However, these errors tend to have a more significant impact on parameter estimation, as the optimization process must reconcile discrepancies between the prescribed model and the true system behavior. Despite these challenges, PINNs show promise in dynamical system modeling, offering a robust approach to handling uncertainties.