Probabilistic Numerics in Python.

Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and custom information operators. Compatible with the broader JAX scientific computing ecosystem.

IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)

Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"

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.

Physics-Enhanced Regression for Initial Value Problems

Computation-Aware Kalman Filtering and RTS Smoothing

Python tools for solving data-constrained finite element problems

Probabilistic numerical finite differences. Compute finite difference weights and differentiation matrices on scattered data sites and with out-of-the-box uncertainty quantification.

Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.

Information and materials for Google Summer of Code participants developing for ProbNum.

Code for the paper "Computation-Aware Kalman Filtering and Smoothing"

Evaluate the accuracy, efficiency, and uncertainty-calibration of probabilistic numerical algorithms.

Probabilistic Linear Solvers for Machine Learning (NeurIPS 2020)

Website of the Probabilistic Numerics community.

Numerical Integration Methods and Probabilistic Methods for generating random numbers.

Various probabilistic numerics projects. Main one is Laplace_Approximation.

Practical session on implementing probabilistic linear solvers at the Probabilistic Numerics Spring School 2024

Published asv benchmark reports and database of ProbNum.