Wenger, Jonathan;
Krämer, Nicholas;
Pförtner, Marvin;
Schmidt, Jonathan;
Bosch, Nathanael;
Effenberger, Nina;
Zenn, Johannes;
... Hennig, Philipp; + view all
(2021)
ProbNum: Probabilistic Numerics in Python.
arXiv: Ithaca (NY), USA.
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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.
| Type: | Working / discussion paper |
|---|---|
| Title: | ProbNum: Probabilistic Numerics in Python |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://arxiv.org/abs/2112.02100v1 |
| Language: | English |
| Additional information: | DOI: 10.48550/arXiv.2112.02100. - For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10158248 |
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