Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis.

ABSTRACT: Describing the material flow stress and the associated uncertainty is essential for the plastic stochastic structural analysis. In this context, a data-driven approach-heteroscedastic sparse Gaussian process regression (HSGPR) with enhanced efficiency is introduced to model the material flow stress. Different from other machine learning approaches, e.g. artificial neural network (ANN), which only estimate the deterministic flow stress, the HSGPR model can capture the flow stress and its uncertainty simultaneously from the dataset. For validating the proposed model, the experimental data of the Al 6061 alloy is used here. Without setting a priori assumption on the mathematical expression, the proposed HSGPR-based flow stress model can produce a better prediction of the experimental stress d

SUBMITTER: Chen B 

PROVIDER: S-EPMC8864002 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature