poscidyn 0.2.1

Fast Simulation of Nonlinear Oscillator Dynamics in Python

Poscidyn (Python oscillator dynamics) is a JAX-based toolkit for batched nonlinear time responses and frequency sweeps. Its supported public workflow uses a solver instance: build the model and excitation once, then call solver.time_response(...) or solver.frequency_sweep(...).

Features include:

• Time-response simulation
• Batched forward and backward synthetic frequency sweeps
• Nonlinear modal oscillators with quadratic and cubic stiffness
• JAX-oriented batching through vmap

Installation

pip install poscidyn[gpu]

Requires Python 3.10 or newer.

Documentation

Have a look at our extensive documentation on how to install, use and extend this package: https://rknetemann.github.io/poscidyn/.

Quick example

import jax.numpy as jnp
import poscidyn

oscillator = poscidyn.NonlinearOscillator(
    omega_0=jnp.array([1.0]),
    Q=jnp.array([80.0]),
    a=jnp.zeros((1, 1, 1)),
    b=jnp.array([[[[0.2]]]]),
)
excitation = poscidyn.DirectHarmonicExcitation(f_d=jnp.array([0.002]))
solver = poscidyn.TimeIntegration(
    oscillator=oscillator,
    excitation=excitation,
    response_measure=poscidyn.Demodulation(),
    multistart=poscidyn.LinearResponse(n_init_cond=8),
    n_time_steps=64,
)
result = solver.frequency_sweep(jnp.linspace(0.8, 1.2, 100))

The documentation provides an executable first sweep, a time-response quickstart, numerical guidance, and a clear distinction between supported features and research directions.

Credits where they are due

JAX: a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.

Diffrax: JAX-based library providing numerical differential equation solvers.

Equinox: your one-stop JAX library, for everything you need that isn't already in core JAX.