Data-driven modeling of complex mechanical components for integration in system-level simulations

Abstract

This contribution presents a data-driven approach featuring a physics-inspired neural network structure for modeling complex components in mecha(tro)nic systems. In the present approach, gated recurrent units (GRUs) are employed to approximate the ordinary differential equations (ODEs) describing the system’s states over time, followed by a deep feedforward neural network (FFNN) mapping these states to a target variable. The networks are shown to predict a latent space capable of modeling the underlying dynamics, without the need for measuring the full state vector and only relying on input-output measurements. Subsequently it is shown that a nonlinear coordinate transformation exists between the latent space of the network and the states obtained from the reference ODE integration (simulation). To have a verification of the network’s performance, it is applied to simulation-based data of an academic example for which the states and equations are known beforehand. Furthermore, the methodology is also applied to real measurement data from an INSTRON testing system capturing shock damper and bushing dynamic behaviour. Lastly, it is demonstrated that an ODE expression can be extracted from the trained network. This feature allows seamless integration of these networks into variable time-step, system-level simulation software.

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