The Configurational Element Method for Nonconvex Granular Media

2026-02-28

AI Summary

Efficient simulation of granular media composed of non-convex particles has long been hindered by high computational complexity and challenges in contact modeling. This work proposes a novel approach that, for the first time, integrates geometric contact relationships in configuration space with neural networks to learn a contact mapping. By doing so, it transforms the contact detection problem for rigid non-convex particles into an efficient neural inference process. The method substantially reduces computational overhead, enabling fast, robust, and large-scale dynamic simulations of non-convex particle systems on standard hardware, thereby significantly enhancing both the feasibility and efficiency of such simulations.

Technology Category

• Search and Optimization: Non-convex Optimization
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Computational social science

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.

Problem

• non-convex granular media
• geometric contact
• simulation
• rigid grains
• computational complexity

Innovation

• non-convex granular media
• configuration space
• neural network contact modeling
• rigid body simulation
• computational efficiency