|
In this article, the power of physics-informed neural networks is employed to address the issue of model identification for complex physical systems, focusing on the application of a magnetorheological (MR) damper setup. The research leverages the Bouc-Wen hysteresis model, a well-established representation of nonlinear behavior in MR dampers, to inform the training process of a series of cascaded neural networks. The core objective of this research is to develop a surrogate model capable of accurately predicting the dynamic behavior of MR dampers under various operational conditions. Traditionally, MR dampers pose significant modelling challenges due to their nonlinear and hysteresis-rich characteristics. The approach explored in this article combines physics-based insights with the capabilities of neural networks to resolve the complexity associated with the modelling process. The methodology involves the formulation of a physics-informed loss function, which embeds the Bouc-Wen hysteresis model’s governing equations into the training process of the neural networks. This fusion of physical principles and machine learning enables the networks to inherently capture the underlying physics, resulting in a more accurate and interpretable surrogate model. Through experimentation, the effectiveness of the physics-informed neural network approach in surrogate modeling for MR dampers is demonstrated. The model developed exhibits decent predictive performance across a range of input parameters and excitation conditions, offering a promising alternative to conventional black-box machine learning and physics-based methods. Furthermore, this research showcases the potential for physics-informed machine learning in modelling complex physical systems, offering a perspective on the utility of this approach in other engineering and scientific domains. The application of this methodology further facilitates improved control and optimization strategies in various engineering applications.
|
