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

Abstract

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 50 ms. Ridge and Random Forest predict the maximum penetration. A single feature (the intruder height at 50 ms) concentrates almost all predictive power, showing that density information is already encoded in the early impact response.