Prediction of the maximum penetration of a circular intruder in a two-dimensional granular bed from its early trajectory using Machine Learning

We test whether the maximum penetration depth of a circular intruder into a two-dimensional granular bed can be predicted from its early trajectory using machine learning, without giving the intruder density. From 100 DEM simulations with varying density, we extract kinematic features from the first