Open Source Projects

ProbNumDiffEq.jl provides probabilistic numerical ODE solvers to the DifferentialEquations.jl ecosystem. The implemented ODE filters solve differential equations via Bayesian filtering and smoothing. The filters compute not just a single point estimate of the true solution, but a posterior distribution that contains an estimate of its numerical approximation error.

Try it out: ] add ProbNumDiffEq.jl

ProbNum is a Python toolkit for solving numerical problems in linear algebra, optimization, quadrature and differential equations. ProbNum solvers not only estimate the solution of the numerical problem, but also its uncertainty (numerical error) which arises from finite computational resources, discretization and stochastic input. This numerical uncertainty can be used in downstream decisions.

To use: pip install probnum

Parallel-in-Time ODE Filters in Jax

A light-weight library to help you create better plots for scientific publications, by taking care of the annoying bits like figure size, font size, and setting the correct font, with minimal overhead.

Handy code for Gaussian filtering and smoothing enthousiasts.

Positive semi-definite matrix types in Julia

A collection of chaotic ODEs.

Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.com/nathanaelbosch/ProbNumDiffEq.jl for Julia code.

PyTorch Implementation: "Optimizing the Latent Space of Generative Networks"