Data generation framework for inverse modeling of nonlinear systems in structural dynamics applications

Abstract

In structural dynamics, response modeling relies on parameters, which are to be identified by experiments. However, for satisfactory results, the design of such experiments is laborious and requires a comprehensive physical insight, which is limited. Furthermore, accurate models are high dimensional and can operate only with a large set of parameters, which increases the experimental effort even more. Efficient data sampling methods have been addressed in studies within areas of design of experiments and active learning. However, generating a data set for nonlinear dynamic systems poses an increased degree of difficulty, since the system needs to be guided through unknown dynamics to collect the desired data points. In this paper, we address this challenge by introducing a theoretical data generation framework for testing-integrated modeling. In the proposed framework, we use feedforward neural networks (FNNs) for inverse modeling of the nonlinear restoring force of the systems. By sequentially evaluating the accuracy of the trained model on a given test data set, the excitation signal applied on the system is adapted to generate optimal response data which allow the FNN model to learn the restoring force behavior. Hence, data generation is posed as an optimization problem and pattern search algorithm is used for sampling. The performance of the proposed framework is evaluated, and it is shown that it outperforms unsupervised sampling methods.