Integrating Entropy-based Data Reduction and Machine Learning in Multidisciplinary Engineering Systems for Enhanced Response Prediction

Abstract

This research presents a framework that aims to capture and model streamlined design variables in multidisciplinary engineering systems, particularly when uncertainties are present. In multidisciplinary domains, there may be correlated design variables that are surplus, leading to data redundancy and potentially affecting the prediction of system responses. To address this issue, the framework utilizes data reduction techniques based on the correlation degree of random design variables, which are evaluated using an entropybased correlation coefficient (e). By doing so, the framework enables a more precise prediction of system responses. The data reduction process is dependent on the value of e and employs two distinct approaches. For strong correlations (high e values), feature extraction techniques such as Principal Component Analysis and the Auto-encoder algorithm are applied. On the other hand, for weak correlations, feature selection is implemented using the Independent Features Test. To effectively predict the complex responses of multidisciplinary systems while enhancing computational efficiency, the framework integrates an Artificial Neural Network. The efficacy of the proposed framework is demonstrated through examples, including a cantilever beam with randomly distributed materials and an electro-mechanical stretchable patch antenna.