<p>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.</p>