Examples

These application recipes build on the component tutorials in the User guide. The tutorials explain the API; these recipes combine it into simulation and analysis workflows. Both sets of pages are generated from Python sources in the repository’s examples directory.

The scripts use small, reproducible configurations for a short first run. Their particle counts and iteration limits are teaching defaults, not production settings or evidence of bulk behavior. Each recipe explains its observables and any checks needed before interpreting the result.

The figures are regenerated from each run. ParaView renders particles, meshes, contact networks and saved simulation frames; Matplotlib shows diagnostics such as energy, contact pressure, spectra and convergence. Short movies have playback controls, and selected scenes can be explored interactively in 3D.

Construction & packing pipelines

• Polydisperse sphere packing in one call
• Catch-all builder: polydisperse GA/DP packings in one call
• Rigid geometric-asperity clumps: step-by-step pipeline
• Geometric-asperity deformable particles: step-by-step pipeline
• Minimization of bidisperse spheres (or disks).
• Follow the energy during minimization
• Jamming of bidisperse spheres (or disks).
• Jammed bidisperse packing: contacts, rattlers, and the dynamical matrix
• Jammed rigid clumps: contacts, rattlers, dynamical matrix, and friction
• Find rattlers and draw a contact network
• Packing-fraction protocol: integrate with a scheduled box rescale
• Save selected quantities on a pseudo-logarithmic schedule
• SASA of a rigid clump via a spherical tracer
• Particle-level friction coefficient of a rigid clump

Dynamics & active matter

• A rotating drum loaded from an STL mesh
• Temperature and density control
• Constant-rate shear below a finite packing’s jamming threshold
• Quasistatic shear of a small jammed disk packing
• Vicsek alignment of rigid dumbbells

Inference, deformation & ensembles

These short applications connect mechanics to measured outcomes: fitting a contact parameter, comparing constitutive response, following a rearrangement, and quantifying preparation dependence across independent simulations.

• Infer contact stiffness from observed trajectories
• Rigid, elastic and viscoplastic grains under the same load
• Watch a soft mode develop into a rearrangement
• Run an ensemble of granular compaction experiments

Reinforcement learning

Start with the training tutorial for the optional, experimental reinforcement-learning subsystem.

• Drive vectorized environments with a custom policy
• Scenario data