Inverse mapping parameter updating with feature selection based on measured data

To minimize the mismatch between a physical system and its digital twin (constituted by a model), model updating can be applied. In this work, an efficient, online model updating method is employed to update interpretable parameter values of nonlinear dynamics models. This method uses inverse mapping models that map a selected set of measured features to a set of parameter values. To do so, the inverse mapping model is constituted by a neural network that is trained based on simulated (training) data using
supervisory learning. Here, the set of selected features is obtained by employing (joint) mutual information-based feature selection. To illustrate the method, it is applied to a high-tech industrial use case for which measurements are collected from a wire bonder. Here, it is shown that the updated models have higher predictive capacity than a reference model of which parameters are manually determined.