Granular Flow and Deposition Generative Modeling: A Physics-Driven Framework for Three-Dimensional Shape Generation Through Discrete Particle Dynamic
Abstract
Three-dimensional generative modeling has increasingly explored representations such as point clouds, voxels, implicit surfaces, neural radiance fields, Gaussian splatting, and diffusion-based models. However, the mechanisms used to produce a target geometry are often separated from the physical processes through which particulate structures emerge in the real world. Sand dunes, gravel piles, powder deposits, and granular slopes are formed through the coupled effects of gravity, particle collisions, friction, rolling, settling, and collective mechanical stability. This paper proposes Granular Flow and Deposition Generative Modeling (GFDGM), a conceptual framework that treats discrete granular dynamics as an active computational mechanism for generating three-dimensional shapes.
In GFDGM, a target shape or a set of geometric constraints guides the release, movement, deposition, and redistribution of particles. Individual particles are represented by geometric and mechanical states, while their interactions determine the evolving configuration of the aggregate. A target-aware control mechanism adjusts particle injection, source locations, boundary conditions, material parameters, and deposition schedules. Rather than stopping solely after a prescribed number of generation steps, the framework evaluates geometric agreement, kinetic activity, contact forces, and mechanical stability. The final particle assembly can be retained as a discrete representation or converted into a surface mesh, voxel volume, point cloud, or implicit surface.
This paper formalizes the framework, presents its governing equations, defines a generation algorithm, and proposes an evaluation protocol for comparison with procedural modeling, conventional discrete element simulations, and learning-based generative models. Potential applications include terrain synthesis, granular object generation, digital geological environments, and physically constrained three-dimensional asset creation. The contribution is a proposed integration of target-conditioned shape generation, granular process control, and stability-aware termination. The framework is conceptual and requires implementation and empirical validation; it does not claim that discrete element modeling or generative modeling of granular materials is itself new.
Related
- Layerwise Deposition-Field Generative Modeling: A Process-Conditioned Framework for Three-Dimensional Generation Through Thin-Film Deposition and Surface Growth
- Deposition-Based 3D Generative Modeling: A Framework for Three-Dimensional Object Generation Through Gas-Phase Particle Deposition, Local Adsorption, and Branching Growth
- Fluvial Erosion–Deposition Generative Model (FED-GM): A Process-Driven Framework for Three-Dimensional Terrain Generation Through Sediment Transport and Geomorphic Feedback
- Granular Deposition Image Generation: A Framework for Target-Guided Visual Synthesis Through Particle Transport, Selective Deposition, and Dynamic Reorganization
- Reaction-Driven Three-Dimensional Generative Computing: A Framework for Constructing 3D Objects Through Local Chemical Transformation, Deposition, and Structural Growth
BibTeX
@misc{zhang2026granular,
title = {{Granular Flow and Deposition Generative Modeling: A Physics-Driven Framework for Three-Dimensional Shape Generation Through Discrete Particle Dynamic}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23636024},
url = {https://zenodo.org/records/23636024},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Granular Flow and Deposition Generative Modeling: A Physics-Driven Framework for Three-Dimensional Shape Generation Through Discrete Particle Dynamic
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/25
PB - Zenodo
DO - 10.5281/zenodo.23636024
UR - https://zenodo.org/records/23636024
AB - Three-dimensional generative modeling has increasingly explored representations such as point clouds, voxels, implicit surfaces, neural radiance fields, Gaussian splatting, and diffusion-based models. However, the mechanisms used to produce a target geometry are often separated from the physical processes through which particulate structures emerge in the real world. Sand dunes, gravel piles, powder deposits, and granular slopes are formed through the coupled effects of gravity, particle collisions, friction, rolling, settling, and collective mechanical stability. This paper proposes Granular Flow and Deposition Generative Modeling (GFDGM), a conceptual framework that treats discrete granular dynamics as an active computational mechanism for generating three-dimensional shapes. In GFDGM, a target shape or a set of geometric constraints guides the release, movement, deposition, and redistribution of particles. Individual particles are represented by geometric and mechanical states, while their interactions determine the evolving configuration of the aggregate. A target-aware control mechanism adjusts particle injection, source locations, boundary conditions, material parameters, and deposition schedules. Rather than stopping solely after a prescribed number of generation steps, the framework evaluates geometric agreement, kinetic activity, contact forces, and mechanical stability. The final particle assembly can be retained as a discrete representation or converted into a surface mesh, voxel volume, point cloud, or implicit surface. This paper formalizes the framework, presents its governing equations, defines a generation algorithm, and proposes an evaluation protocol for comparison with procedural modeling, conventional discrete element simulations, and learning-based generative models. Potential applications include terrain synthesis, granular object generation, digital geological environments, and physically constrained three-dimensional asset creation. The contribution is a proposed integration of target-conditioned shape generation, granular process control, and stability-aware termination. The framework is conceptual and requires implementation and empirical validation; it does not claim that discrete element modeling or generative modeling of granular materials is itself new.
ER -