probabilistic-numerics

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

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

Python tools for solving data-constrained finite element problems

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

Gaussian process PDE solvers that combine linear PDEs with linear inequality constraints and solve complex-valued Helmholtz field problems.

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

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

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

Probabilistic Linear Solvers for Machine Learning (NeurIPS 2020)

Tutorial for the International Conference on Probabilistic Numerics 2026

Website of the Probabilistic Numerics community.

Code for the manuscript "A probabilistic diagnostic for Laplace approximations: Introduction and experimentation"

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