ProbNum: Probabilistic Numerics in Python
This work provides a tool for researchers and practitioners in computational fields to apply PNMs with uncertainty quantification, but it is incremental as it focuses on software implementation rather than new algorithmic breakthroughs.
The authors tackled the challenge of implementing probabilistic numerical methods (PNMs) for solving numerical problems like linear algebra and optimization by developing ProbNum, a Python library that provides state-of-the-art solvers and allows custom composition, with resources such as tutorials and benchmarks available online.
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.