ode-filters 0.7.1

Probabilistic ODE solvers using square-root Gaussian filtering and smoothing in JAX.

ODE Filters

A JAX-based implementation of probabilistic ODE solvers using Gaussian filtering and smoothing. This package provides tools for solving ordinary differential equations while quantifying uncertainty through Bayesian inference.

Features

• Pure JAX implementation - Fully differentiable and JIT-compilable
• Square-root filtering - Numerically stable EKF and RTS smoothing
• Flexible priors - Integrated Wiener Process (IWP), Matern, and joint priors
• First and second-order ODEs - Native support for both ODE types
• Constraint handling - Conservation laws and time-varying measurements
• State-parameter estimation - Joint inference with hidden states
• Black-box measurements - Custom observation models with autodiff Jacobians
• Transformed measurements - Nonlinear state transformations with chain-rule Jacobians
• Pluggable linearization - EK0 / EK1 / IEKF corrections, selectable per solve
• Adaptive step sizes - jit/vmap/grad-safe adaptive solving, with optional fixed-point smoothing
• Parameter estimation - Differentiable marginal likelihood with an Optax-friendly fit API

Installation

Install the latest release from PyPI:

pip install ode-filters

Or install from source with development dependencies:

git clone https://github.com/paufisch/ode_filters.git
cd ode_filters
pip install -e ".[dev]"

Quick Example

import jax.numpy as np
from ode_filters import (
    IWP,
    ODEInformation,
    gaussian_filter,
    rts_smoother,
    taylor_mode_initialization,
)

# Define ODE: dx/dt = -x (exponential decay)
def vf(x, *, t):
    return -x

x0 = np.array([1.0])
tspan = (0.0, 5.0)  # a tuple: hashable for use as a jax.jit static argument

# Set up the prior and the ODE-information measurement model
prior = IWP(q=2, d=1, Xi=0.5 * np.eye(1))
mu_0, Sigma_0_sqr = taylor_mode_initialization(vf, x0, q=2)
measure = ODEInformation(vf, prior.E0, prior.E1)

# Filter on a fixed grid, then smooth
result = gaussian_filter(mu_0, Sigma_0_sqr, prior, measure, tspan, N=50)
m_smooth, P_smooth_sqr = rts_smoother(prior, result)

# result.m / result.P_sqr -> filtered means / square-root covariances at the grid
# result.log_likelihood   -> calibrated marginal log-likelihood

For adaptive step sizes, use gaussian_filter_adaptive(mu_0, Sigma_0_sqr, prior, measure, save_at=...) — it is jit / vmap / grad-safe; pass smoother=True to also get a fixed-point smoothing pass that rts_smoother consumes.

Package Structure

ode_filters/
├── filters/          # EKF and RTS smoothing loops
├── inference/        # Square-root Gaussian algebra
├── measurement/      # ODE and observation models
└── priors/           # Gaussian Markov process priors

Documentation

Full documentation is available at paufisch.github.io/ode_filters.

Development

Run the test suite:

uv run pytest --cov=ode_filters --cov-report=term-missing

Build documentation locally:

uv run mkdocs serve

License

MIT License - see LICENSE for details.