CUQIpy: I. Computational uncertainty quantification for inverse problems in Python

Research output: Contribution to journalJournal articleResearchpeer-review

40 Downloads (Pure)

Abstract

This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series. CUQIpy employs a Bayesian framework, integrating prior knowledge with observed data to produce posterior probability distributions that characterize the uncertainty in computed solutions to inverse problems. The package offers a high-level modeling framework with concise syntax, allowing users to easily specify their inverse problems, prior information, and statistical assumptions. CUQIpy supports a range of efficient sampling strategies and is designed to handle large-scale problems. Notably, the automatic sampler selection feature analyzes the problem structure and chooses a suitable sampler without user intervention, streamlining the process. With a selection of probability distributions, test problems, computational methods, and visualization tools, CUQIpy serves as a powerful, flexible, and adaptable tool for UQ in a wide selection of inverse problems. Part II of the series focuses on the use of CUQIpy for UQ in inverse problems with partial differential equations.
Original languageEnglish
Article number045009
JournalInverse Problems
Volume40
Issue number4
Number of pages37
ISSN0266-5611
DOIs
Publication statusPublished - 2024

Keywords

  • Uncertainty quantification
  • Software
  • Computational imaging
  • Bayesian statistics
  • Probabilistic programming

Fingerprint

Dive into the research topics of 'CUQIpy: I. Computational uncertainty quantification for inverse problems in Python'. Together they form a unique fingerprint.

Cite this