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

TL;DR

This paper presents an extended tensor decomposition method that enhances model reduction accuracy for nonlinear and uncertain problems, enabling fast simulations in manufacturing and material design.

Contribution

The paper introduces a novel extended tensor decomposition (XTD) with sparse enrichment, improving approximation and reducibility in complex nonlinear problems.

Findings

XTD improves approximation accuracy in nonlinear cases

XTD enables real-time multi-parametric simulations

Comparison shows XTD outperforms finite element analysis in efficiency

Abstract

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 has been 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.