The proposed study develops a framework that accurately captures and models input and output variables for multidisciplinary systems in order to mitigate the computational cost when uncertainties are involved. Under this framework, the dimension of the random input variables is reduced depending on the degree of correlation calculated by an entropy based correlation coefficient (e). According to the obtained value of e, the dimension is truncated by two different methods. First feature extraction methods, namely Principal Component Analysis and the Auto-Encoder algorithm, are utilized when the input variables are highly correlated. In contrast, the Independent Features Test is implemented as the feature selection method if the correlation is too low to select a critical subset of model features. An Artificial Neural Network, including a Probabilistic Neural Network, is integrated into the framework to correctly capture the complex response behavior of the multidisciplinary system with low computational cost. The efficacy of the proposed method is demonstrated with electro-mechanical engineering examples, including a solder joint and a stretchable patch antenna.

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