A polynomial chaos approach to predicting swimming performance

Abstract

Jellyfish only expend little energy while in motion, making them highly efficient swimmers. We can explore swimming performance using computer simulations to further understand jellyfish locomotion. Each computer simulation is time consuming and costly for data storage, making it difficult to identify optimal swimming conditions. We aimed to address these challenges through a machine learning method called polynomial chaos expansions (PCE). The research question of this study is as follows: How can polynomial chaos reduce simulation time and predict swimming performance?