NeuralDEM for real time simulations of industrial particular flows

The discrete element method (DEM) is a highly accurate and versatile approach for modeling large-scale particulate and fluid-mechanical systems critical to industrial processes. Additionally, DEM offers integration with grid-based computational fluid dynamics, making DEM a key ingredient for the modeling of many multi-physics systems. However, its computational demands, driven by the multiscale nature of these systems, limit simulation scale and duration. To address this, we introduce NeuralDEM, a fast and adaptable deep learning surrogate that captures long-term transport processes across various regimes using macroscopic observables, without relying on microscopic model parameters. NeuralDEM is a deep learning approach scalable to real-time industrial applications. Such scenarios have previously been challenging for deep learning models. NeuralDEM will open many doors to advanced engineering and much faster process cycles. The discrete element method (DEM) is crucial for modeling complex particulate systems but is limited by high computational demands. Here, the authors introduce NeuralDEM, a deep learning surrogate that enables real-time simulations by capturing fine-grained particle dynamics using a neural field model, significantly advancing engineering applications and accelerating process cycles across industries.