A comparative analysis of dimensionality reduction surrogate modeling techniques for full human body finite element impact simulations

Abstract

Fast-running surrogate computational models (simpler computational models) have been successfully used to replace time-intensive finite element models. However, it is unclear how well they perform in accurately and efficiently replicating complex, full human body finite element models. Here we survey several surrogate modeling techniques and assess their accuracy in predicting full strain fields of tissues of interest during a highly dynamic behind armor blunt trauma impact to the liver. We found that coupling dimensionality reduction on the high-dimensional output space (principal component analysis or autoencoders) with machine learning techniques (Gaussian Process Regression or multi-output neural networks) provides a framework capable of accurately and efficiently replacing complex full human body models. It was found that these surrogate models can successfully predict the strain fields (<10% average strain error) of select tissues during a nonlinear impact event but careful consideration should be given to element parsing and modeling technique.