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ProbNum: Probabilistic Numerics in Python

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arxiv 2112.02100 v1 pith:A5DB67KO submitted 2021-12-03 cs.MS cs.LGcs.NAmath.NA

Jonathan Wenger , Nicholas Krämer , Marvin Pförtner , Jonathan Schmidt , Nathanael Bosch , Nina Effenberger , Johannes Zenn , Alexandra Gessner
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Toni Karvonen François-Xavier Briol Maren Mahsereci Philipp Hennig
This is my paper
classification cs.MScs.LGcs.NAmath.NA
keywords probabilisticprobnumnumericalpnmsproblempythonwellalgebra
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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.

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  1. rodeo: Probabilistic Methods of Parameter Inference for Ordinary Differential Equations

    stat.CO 2025-06 conditional novelty 5.0 of 10

    rodeo is a JAX-based Python library that implements probabilistic ODE solvers and several Bayesian parameter inference methods with linear scaling in system size.

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