Extended tensor decomposition model reduction methods: Training, prediction, and design under uncertainty

This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tensor decomposition, which is expected to improve the approximation accuracy and the reducibility (compressibility) in highly nonlinear and singular cases. The proposed XTD method can be a powerful tool for solving nonlinear space–time parametric problems. The method has been successfully applied to parametric elastic–plastic problems and real time additive manufacturing residual stress predictions with uncertainty quantification. Furthermore, a combined XTD-SCA (self-consistent clustering analysis) strategy is presented for multi-scale material modeling, which enables real time multi-scale multi-parametric simulations. The efficiency of the method is demonstrated with comparison to finite element analysis. The proposed method enables a novel framework for fast manufacturing and material design with uncertainties. •Novel eXtended Tensor Decomposition (XTD) based nonlinear model reduction method.•Sparse non-separated enrichment can improve the accuracy, convergence, and robustness of model reduction methods.•XTD has been applied to nonlinear elastic–plastic problems for stress predictions in additive manufacturing.•The combined XTD-SCA (self-consistent clustering analysis) can enable fast multiscale material modeling and design under uncertainty.