The Configurational Element Method for Nonconvex Granular Media

TL;DR

This paper introduces a neural network-based method to efficiently simulate large-scale systems of non-convex granular media by modeling geometric contact in configuration space, improving computational robustness and speed.

Contribution

The paper presents a novel approach that simplifies the simulation of non-convex grains using neural networks to model contact maps, enabling faster and more robust large-scale simulations.

Findings

Efficient simulation of non-convex grains achieved

Neural network contact map accurately predicts interactions

Method reduces computational complexity for large systems

Abstract

Granular media surround us, comprising everything from the ground we walk on to the foods we eat. Owing to their ubiquity our ability to understand and predict the mechanical evolution of grains is not only of key scientific importance, but is also a key component to synthesize believable animations of our world. Despite their importance, shortcomings persist in our ability to simulate granular media. In particular, simulating grains with non-convex shapes remains a challenging and computationally expensive task. We propose a method to simulate non-convex rigid grains by posing geometric contact in configuration space and learning the resulting contact map with a neural network. Our formulation reduces the complex task of modeling and simulating non-convex shapes to simple network evaluations that are easily run on standard compute hardware, allowing us to quickly and robustly simulate large scale systems of non-convex grains.