Particle-based processes play a pivotal role in a wide range of engineering applications, from powder synthesis and granular flow handling to advanced manufacturing of energy storage materials. The increasing complexity of these processes, especially in high-value applications such as lithium-ion battery electrode production, demands modeling frameworks (e.g., DEM, FEM, CFD, SPH, LBM) that can integrate physical understanding with data-driven insights. Hybrid modeling, which synergistically combines first-principles simulations with machine learning and artificial intelligence, has emerged as a promising paradigm to address these challenges. This minisymposium will focus on recent advances in computational methods for hybrid modeling of particle-laden and particulate-based processes across multiple scales, with a special emphasis on their application to the handling of particulate materials. Topics of interest include the integration of discrete and continuum models with AI/ML for predictive accuracy and generalization; multiscale and multiphysics particulate system simulation from nano to macro scale; hybrid digital twins for process optimization and control; data-driven discovery of governing laws in particle technology using symbolic regression and physics-informed neural networks; simulation-based design and optimization of particle processes in battery manufacturing, additive manufacturing, and powder metallurgy; and uncertainty quantification and validation frameworks for hybrid models , including emerging AI approaches such as Large Language Models (LLM) and Mixture of Experts in bulk solids processing. This minisymposium will bring together experts from computational mechanics, process engineering, particle technology, and data science to develop robust and interpretable modeling frameworks for complex particulate processes. We particularly welcome contributions on novel computational strategies, experimental–computational integration, and industrial applications.