ProbNum: Probabilistic Numerics in Python

TL;DR

ProbNum is a Python library that offers advanced probabilistic numerical solvers for various computational problems, integrating prior knowledge and uncertainty quantification to improve numerical analysis.

Contribution

This paper introduces ProbNum, a modular Python library that provides state-of-the-art probabilistic numerical methods for diverse problem classes.

Findings

Provides a flexible, modular Python library for probabilistic numerics

Includes tutorials, documentation, and benchmarks for users

Enables custom composition of numerical solvers with uncertainty quantification

Abstract

Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior information about a problem and quantify uncertainty due to finite computational resources as well as stochastic input. In this paper, we present ProbNum: a Python library providing state-of-the-art probabilistic numerical solvers. ProbNum enables custom composition of PNMs for specific problem classes via a modular design as well as wrappers for off-the-shelf use. Tutorials, documentation, developer guides and benchmarks are available online at www.probnum.org.