Machine learning acceleration of granular and solid-fluid flow simulations: A review

The acceleration of physics-based simulations via machine learning has contributed to applications that rely on rapid predictions, such as model predictive control and real-time optimization. Among physics-based simulations, granular flow simulations are of particular interest due to their ubiquity in industrial processes and the high computational cost of traditional models. In this review article, we comment on the various approaches for creating machine learning-based surrogate models from discrete element method (DEM) simulations. This review is specifically focused on methods that accelerate simulations or those that produce the same outputs as high-fidelity models, such as particle positions or void fraction fields. For each method, the underlying concepts behind it were explained and the research articles that used it were discussed. The strengths and weaknesses of each approach in relation to the others were also outlined. After this, knowledge gaps and limitations in the development of surrogate models for granular flow simulations and their practical implementation in industrial applications are presented. Potential solutions to each limitation were suggested based on developments from adjacent fields of study, and these may be taken as directions for future work.