jaxdem.colliders.naive#

Naive \(O(N^2)\) collider implementation.

Classes

NaiveSimulator()

Implementation that computes forces and potential energies using a naive \(O(N^2)\) all-pairs interaction loop.
class jaxdem.colliders.naive.NaiveSimulator[source]#

Bases: Collider

Implementation that computes forces and potential energies using a naive \(O(N^2)\) all-pairs interaction loop.

Notes

Due to its \(O(N^2)\) complexity, NaiveSimulator is suitable for simulations with a relatively small number of particles. For larger systems, a more efficient spatial partitioning collider should be used. However, this collider should be the fastest option for small systems (\(<1k-5k\) spheres depending on the GPU).

static compute_potential_energy(state: State, system: System) → jax.Array[source][source]#

Computes the potential energy associated with each particle using a naive \(O(N^2)\) all-pairs loop.

This method iterates over all particle pairs (i, j) and sums the potential energy contributions computed by the system.force_model.

Parameters:

  • state (State) – The current state of the simulation.

  • system (System) – The configuration of the simulation.

Returns:

One-dimensional array containing the total potential energy contribution for each particle.

Return type:

jax.Array

Note

  • This method donates state and system

static compute_force(state: State, system: System) → Tuple['State', 'System'][source][source]#

Computes the total force acting on each particle using a naive \(O(N^2)\) all-pairs loop.

This method sums the force contributions from all particle pairs (i, j) as computed by the system.force_model and updates the particle forces.

Parameters:

  • state (State) – The current state of the simulation.

  • system (System) – The configuration of the simulation.

Returns:

A tuple containing the updated State object with computed forces and the unmodified System object.

Return type:

Tuple[State, System]

Note

  • This method donates state and system